# AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys

arXiv: https://arxiv.org/abs/2510.26012 · HTML: https://mlsanity.com/paper/2510.26012/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 51 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Builds on (most cited first, 30 of 31)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://mlsanity.com/paper/2302.00093/) (2023)
- [Extending Context Window of Large Language Models via Positional Interpolation](https://mlsanity.com/paper/2306.15595/) (2023)
- [PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization](https://mlsanity.com/paper/2306.05087/) (2023)
- [Long-context LLMs Struggle with Long In-context Learning](https://mlsanity.com/paper/2404.02060/) (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)
- [On the Theoretical Limitations of Embedding-Based Retrieval](https://mlsanity.com/paper/2508.21038/) (2025)
- [LLM4SR: A Survey on Large Language Models for Scientific Research](https://mlsanity.com/paper/2501.04306/) (2025)
- [A Practical Survey on Zero-shot Prompt Design for In-context Learning](https://mlsanity.com/paper/2309.13205/) (2023)
- [Prompt Design and Engineering: Introduction and Advanced Methods](https://mlsanity.com/paper/2401.14423/) (2024)
- [PaperRobot: Incremental Draft Generation of Scientific Ideas](https://mlsanity.com/paper/1905.07870/) (2019)
- [Training Sparse Mixture Of Experts Text Embedding Models](https://mlsanity.com/paper/2502.07972/) (2025)
- [Discourse-Aware Neural Rewards for Coherent Text Generation](https://mlsanity.com/paper/1805.03766/) (2018)
- [Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models](https://mlsanity.com/paper/2404.13919/) (2024)
- [ResearchArena: Benchmarking Large Language Models' Ability to Collect and Organize Information as Research Agents](https://mlsanity.com/paper/2406.10291/) (2024)
- [Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts](https://mlsanity.com/paper/2308.10410/) (2023)
- [AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers](https://mlsanity.com/paper/2410.01824/) (2024)
- [Potential and Perils of Large Language Models as Judges of Unstructured Textual Data](https://mlsanity.com/paper/2501.08167/) (2025)
- [Instruct Large Language Models to Generate Scientific Literature Survey Step by Step](https://mlsanity.com/paper/2408.07884/) (2024)
- [VectorSearch: Enhancing Document Retrieval with Semantic Embeddings and Optimized Search](https://mlsanity.com/paper/2409.17383/) (2024)
- [Relevance Filtering for Embedding-based Retrieval](https://mlsanity.com/paper/2408.04887/) (2024)
- [SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension](https://mlsanity.com/paper/2508.01959/) (2025)
- [SurveyGen: Quality-Aware Scientific Survey Generation with Large Language Models](https://mlsanity.com/paper/2508.17647/) (2025)
- [Deep Literature Survey Automation with an Iterative Workflow](https://mlsanity.com/paper/2510.21900/) (2025)
- [Recurrent Binary Embedding for GPU-Enabled Exhaustive Retrieval from Billion-Scale Semantic Vectors](https://mlsanity.com/paper/1802.06466/) (2018)

## Built on by (most cited first, 8 of 8)

- [PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing](https://mlsanity.com/paper/2604.05018/) (2026)
- [SciFig: Towards Automating Editable Figure Generation for Scientific Papers](https://mlsanity.com/paper/2601.04390/) (2026)
- [Agentic AI-Empowered Dynamic Survey Framework](https://mlsanity.com/paper/2602.04071/) (2026)
- [Can AI Review Improve Paper Drafting? An Empirical Study on 20 Computer Architecture Submissions](https://mlsanity.com/paper/2606.01013/) (2026)
- [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)
- [DeepSurvey: Agent-Oriented Automated Survey Generation with Analytical Depth and Citation Reliability](https://mlsanity.com/paper/2605.29522/) (2026)
- [Paper Pilot: A Human-in-the-Loop Expert System for Evidence-Traceable Scientific Manuscript Generation in Applied Sciences](https://mlsanity.com/paper/2608.28596/) (2026)

## Abstract (arXiv)

The rapid growth of research literature, particularly in large language models (LLMs), has made producing comprehensive and current survey papers increasingly difficult. This paper introduces autosurvey2, a multi-stage pipeline that automates survey generation through retrieval-augmented synthesis and structured evaluation. The system integrates parallel section generation, iterative refinement, and real-time retrieval of recent publications to ensure both topical completeness and factual accuracy. Quality is assessed using a multi-LLM evaluation framework that measures coverage, structure, and relevance in alignment with expert review standards. Experimental results demonstrate that autosurvey2 consistently outperforms existing retrieval-based and automated baselines, achieving higher scores in structural coherence and topical relevance while maintaining strong citation fidelity. By combining retrieval, reasoning, and automated evaluation into a unified framework, autosurvey2 provides a scalable and reproducible solution for generating long-form academic surveys and contributes a solid foundation for future research on automated scholarly writing. All code and resources are available at https://github.com/annihi1ation/auto_research.