# InteractiveSurvey: An LLM-based Personalized and Interactive Survey Paper Generation System

arXiv: https://arxiv.org/abs/2504.08762 · HTML: https://mlsanity.com/paper/2504.08762/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 17 of 17)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Extending Context Window of Large Language Models via Positional Interpolation](https://mlsanity.com/paper/2306.15595/) (2023)
- [Enabling Large Language Models to Generate Text with Citations](https://mlsanity.com/paper/2305.14627/) (2023)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022)
- [LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods](https://mlsanity.com/paper/2412.05579/) (2024)
- [MinerU: An Open-Source Solution for Precise Document Content Extraction](https://mlsanity.com/paper/2409.18839/) (2024)
- [Nomic Embed: Training a Reproducible Long Context Text Embedder](https://mlsanity.com/paper/2402.01613/) (2024)
- [AutoSurvey: Large Language Models Can Automatically Write Surveys](https://mlsanity.com/paper/2406.10252/) (2024)
- [LLM4SR: A Survey on Large Language Models for Scientific Research](https://mlsanity.com/paper/2501.04306/) (2025)
- [LitLLM: A Toolkit for Scientific Literature Review](https://mlsanity.com/paper/2402.01788/) (2024)
- [Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning](https://mlsanity.com/paper/2404.08680/) (2024)
- [ChatCite: LLM Agent with Human Workflow Guidance for Comparative Literature Summary](https://mlsanity.com/paper/2403.02574/) (2024)
- [SurveyX: Academic Survey Automation via Large Language Models](https://mlsanity.com/paper/2502.14776/) (2025)
- [A Summarization System for Scientific Documents](https://mlsanity.com/paper/1908.11152/) (2019)
- [Generating a Structured Summary of Numerous Academic Papers: Dataset and Method](https://mlsanity.com/paper/2302.04580/) (2023)
- [PROMPTHEUS: A Human-Centered Pipeline to Streamline SLRs with LLMs](https://mlsanity.com/paper/2410.15978/) (2024)

## Built on by (most cited first, 12 of 12)

- [SciSage: A Multi-Agent Framework for High-Quality Scientific Survey Generation](https://mlsanity.com/paper/2506.12689/) (2025)
- [AI for Auto-Research: Roadmap & User Guide](https://mlsanity.com/paper/2605.18661/) (2026)
- [SurveyG: A Multi-Agent LLM Framework with Hierarchical Citation Graph for Automated Survey Generation](https://mlsanity.com/paper/2510.07733/) (2025)
- [DeepSurvey-Bench: Evaluating Academic Value of Automatically Generated Scientific Surveys](https://mlsanity.com/paper/2601.15307/) (2026)
- [SGSimEval: A Comprehensive Multifaceted and Similarity-Enhanced Benchmark for Automatic Survey Generation Systems](https://mlsanity.com/paper/2508.11310/) (2025)
- [LLM×MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System](https://mlsanity.com/paper/2510.10890/) (2025)
- [SurveyLens: A Discipline-Aware Benchmark for Automatic Survey Generation](https://mlsanity.com/paper/2602.11238/) (2026)
- [SciAtlas: A Computable Atlas of Science for Knowledge-Grounded AI Research](https://mlsanity.com/paper/2605.22878/) (2026)
- [Meow: End-to-End Outline Writing for Automatic Academic Survey](https://mlsanity.com/paper/2509.19370/) (2025)
- [MVSS: A Unified Framework for Multi-View Structured Survey Generation](https://mlsanity.com/paper/2601.09504/) (2026)
- [Deep Academic Survey: Stateful Agentic Closed-Loop Paradigm for Academic Survey Automation](https://mlsanity.com/paper/2608.18034/) (2026)
- [SurveyAgent-HKA: A multi-agent framework for scientific survey generation with LLMs and human knowledge augmentation](https://mlsanity.com/paper/2609.05938/) (2026)

## Abstract (arXiv)

The exponential growth of academic literature creates urgent demands for comprehensive survey papers, yet manual writing remains time-consuming and labor-intensive. Recent advances in large language models (LLMs) and retrieval-augmented generation (RAG) facilitate studies in synthesizing survey papers from multiple references, but most existing works restrict users to title-only inputs and fixed outputs, neglecting the personalized process of survey paper writing. In this paper, we introduce InteractiveSurvey - an LLM-based personalized and interactive survey paper generation system. InteractiveSurvey can generate structured, multi-modal survey papers with reference categorizations from multiple reference papers through both online retrieval and user uploads. More importantly, users can customize and refine intermediate components continuously during generation, including reference categorization, outline, and survey content through an intuitive interface. Evaluations of content quality, time efficiency, and user studies show that InteractiveSurvey is an easy-to-use survey generation system that outperforms most LLMs and existing methods in output content quality while remaining highly time-efficient.