# SF-RAG: Structure-Fidelity Retrieval-Augmented Generation for Academic Question Answering

arXiv: https://arxiv.org/abs/2602.13647 · HTML: https://mlsanity.com/paper/2602.13647/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 19 of its 47 references that are in the map.
- First posted on arXiv 14 Feb 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, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022)
- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024)
- [RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs](https://mlsanity.com/paper/2407.02485/) (2024)
- [LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs](https://mlsanity.com/paper/2406.15319/) (2024)
- [Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training](https://mlsanity.com/paper/2405.20978/) (2024)
- [LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering](https://mlsanity.com/paper/2410.18050/) (2024)
- [DOCBENCH: A Benchmark for Evaluating LLM-based Document Reading Systems](https://mlsanity.com/paper/2407.10701/) (2024)
- [Scim: Intelligent Skimming Support for Scientific Papers](https://mlsanity.com/paper/2205.04561/) (2022)
- [Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking](https://mlsanity.com/paper/2308.07517/) (2023)
- [T-RAG: Lessons from the LLM Trenches](https://mlsanity.com/paper/2402.07483/) (2024)
- [Threddy: An Interactive System for Personalized Thread-based Exploration and Organization of Scientific Literature](https://mlsanity.com/paper/2208.03455/) (2022)
- [Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation](https://mlsanity.com/paper/2402.18150/) (2024)
- [Deciphering the Interplay of Parametric and Non-parametric Memory in Retrieval-augmented Language Models](https://mlsanity.com/paper/2410.05162/) (2024)
- [SuperRAG: Beyond RAG with Layout-Aware Graph Modeling](https://mlsanity.com/paper/2503.04790/) (2025)
- [Marvista: Exploring the Design of a Human-AI Collaborative News Reading Tool](https://mlsanity.com/paper/2207.08401/) (2022)

## Built on by (most cited first, 2 of 2)

- [Benchmarking Complex Multimodal Document Processing Pipelines: A Unified Evaluation Framework for Enterprise AI](https://mlsanity.com/paper/2604.26382/) (2026)
- [ContraVis: Evidence-Grounded Visual Analytics for Contradiction Review in Legal Contracts](https://mlsanity.com/paper/2609.27014/) (2026)

## Abstract (arXiv)

Efficient question-answering (QA) over extensive scientific literature is essential for evidence-based engineering decision-making. Retrieval-augmented generation (RAG) is increasingly applied to question-answering over long academic papers, where accurate evidence allocation under a fixed token budget is critical. However, existing approaches flatten papers into unstructured chunks, destroying the native hierarchical structure and forcing retrieval to operate in a disordered space. This produces fragmented contexts, misallocates tokens to non-evidential regions, and increases the reasoning burden for downstream language models.To address these issues, we propose SF-RAG, an RAG framework that treats the native hierarchical structure of academic papers as a low-entropy retrieval prior.SF-RAG first inherits the native hierarchy to construct a structure-fidelity index, which prevents entropy increase at the source.It then designs a path-guided retrieval mechanism that aligns query semantics to relevant sections and selects high relevance root-to-leaf paths under a fixed token budget, yielding compact, coherent, and low-entropy retrieval contexts.In contrast to existing RAG approaches, SF-RAG avoids entropy increase caused by destructive preprocessing and provides a native low-entropy structural basis for subsequent retrieval. We further introduce entropy-based structural diagnostics to quantify retrieval fragmentation and evidence allocation accuracy.Evaluations across three QA benchmarks show that SF-RAG significantly reduces retrieval fragmentation and improves evidence allocation. These structural benefits drive superior answer quality, establishing a scalable foundation for intelligent engineering document systems and future applications in technical specifications.