{"ID":2848810,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.26012","arxiv_id":"2510.26012","title":"AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys","abstract":"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.","short_abstract":"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 evalua...","url_abs":"https://arxiv.org/abs/2510.26012","url_pdf":"https://arxiv.org/pdf/2510.26012v3","authors":"[\"Siyi Wu\",\"Chiaxin Liang\",\"Ziqian Bi\",\"Leyi Zhao\",\"Tianyang Wang\",\"Junhao Song\",\"Yichao Zhang\",\"Keyu Chen\",\"Benji Peng\",\"Xinyuan Song\"]","published":"2025-10-29T22:57:03Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":607647,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2848810,"paper_url":"https://arxiv.org/abs/2510.26012","paper_title":"AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys","repo_url":"https://github.com/annihi1ation/auto_research","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}