# InsightGUIDE: An Opinionated AI Assistant for Guided Critical Reading of Scientific Literature

arXiv: https://arxiv.org/abs/2509.20493 · HTML: https://mlsanity.com/paper/2509.20493/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 24 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization](https://mlsanity.com/paper/2109.09784/) (2021)
- [LLM4SR: A Survey on Large Language Models for Scientific Research](https://mlsanity.com/paper/2501.04306/) (2025)
- [Scientific Article Summarization Using Citation-Context and Article's Discourse Structure](https://mlsanity.com/paper/1704.06619/) (2017)
- [Generating a Structured Summary of Numerous Academic Papers: Dataset and Method](https://mlsanity.com/paper/2302.04580/) (2023)
- [CORE-GPT: Combining Open Access research and large language models for credible, trustworthy question answering](https://mlsanity.com/paper/2307.04683/) (2023)
- [AI and Generative AI for Research Discovery and Summarization](https://mlsanity.com/paper/2401.06795/) (2024)
- [CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers](https://mlsanity.com/paper/2502.20582/) (2025)

## Abstract (arXiv)

The proliferation of scientific literature presents an increasingly significant challenge for researchers. While Large Language Models (LLMs) offer promise, existing tools often provide verbose summaries that risk replacing, rather than assisting, the reading of the source material. This paper introduces InsightGUIDE, a novel AI-powered tool designed to function as a reading assistant, not a replacement. Our system provides concise, structured insights that act as a "map" to a paper's key elements by embedding an expert's reading methodology directly into its core AI logic. We present the system's architecture, its prompt-driven methodology, and a qualitative case study comparing its output to a general-purpose LLM. The results demonstrate that InsightGUIDE produces more structured and actionable guidance, serving as a more effective tool for the modern researcher.