# TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models

arXiv: https://arxiv.org/abs/2507.18945 · HTML: https://mlsanity.com/paper/2507.18945/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 39 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 20 of 20)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024)
- [Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Models](https://mlsanity.com/paper/2305.11483/) (2023)
- [IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded Feedback](https://mlsanity.com/paper/2410.04025/) (2024)
- [Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols](https://mlsanity.com/paper/2009.14237/) (2020)
- [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)
- [Threddy: An Interactive System for Personalized Thread-based Exploration and Organization of Scientific Literature](https://mlsanity.com/paper/2208.03455/) (2022)
- [CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context](https://mlsanity.com/paper/2302.07302/) (2023)
- [Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections](https://mlsanity.com/paper/2302.06754/) (2023)
- [Scholastic: Graphical Human-Al Collaboration for Inductive and Interpretive Text Analysis](https://mlsanity.com/paper/2208.06133/) (2022)
- [An AI-Resilient Text Rendering Technique for Reading and Skimming Documents](https://mlsanity.com/paper/2401.10873/) (2024)
- [ComLittee: Literature Discovery with Personal Elected Author Committees](https://mlsanity.com/paper/2302.06780/) (2023)
- [Contrastive Hierarchical Discourse Graph for Scientific Document Summarization](https://mlsanity.com/paper/2306.00177/) (2023)
- [ScholarMate: A Mixed-Initiative Tool for Qualitative Knowledge Work and Information Sensemaking](https://mlsanity.com/paper/2504.14406/) (2025)
- [PaperWave: Listening to Research Papers as Conversational Podcasts Scripted by LLM](https://mlsanity.com/paper/2410.15023/) (2024)
- [Papeos: Augmenting Research Papers with Talk Videos](https://mlsanity.com/paper/2308.15224/) (2023)
- [The Design Space of Recent AI-assisted Research Tools for Ideation, Sensemaking, and Scientific Creativity](https://mlsanity.com/paper/2502.16291/) (2025)

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

- [TreeWriter: AI-Assisted Hierarchical Planning and Writing for Long-Form Documents](https://mlsanity.com/paper/2601.12740/) (2026)

## Abstract (arXiv)

Efficiently navigating and understanding academic papers is crucial for scientific progress. Traditional linear formats like PDF and HTML can cause cognitive overload and obscure a paper's hierarchical structure, making it difficult to locate key information. While LLM-based chatbots offer summarization, they often lack nuanced understanding of specific sections, may produce unreliable information, and typically discard the document's navigational structure. Drawing insights from a formative study on academic reading practices, we introduce TreeReader, a novel language model-augmented paper reader. TreeReader decomposes papers into an interactive tree structure where each section is initially represented by an LLM-generated concise summary, with underlying details accessible on demand. This design allows users to quickly grasp core ideas, selectively explore sections of interest, and verify summaries against the source text. A user study was conducted to evaluate TreeReader's impact on reading efficiency and comprehension. TreeReader provides a more focused and efficient way to navigate and understand complex academic literature by bridging hierarchical summarization with interactive exploration.