| title | AI for Auto-Research: Roadmap & User Guide |
|---|---|
| authors | Lingdong Kong, Xian Sun, Wei Chow, Linfeng Li, Kevin Qinghong Lin, Xuan Billy Zhang, Song Wang, Rong Li, Qing Wu, Wei Gao, Yingshuo Wang, Shaoyuan Xie, Jiachen Liu, Leigang Qu, Shijie Li, Lai Xing Ng, Benoit R. Cottereau, Ziwei Liu, Tat-Seng Chua, Wei Tsang Ooi |
| date | 2026-05-18 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.18661 |
| upvotes | 70 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents can execute experiments, draft manuscripts, and simulate critique with minimal human input. Yet this productivity frontier exposes a deeper integrity problem: under scientific pressure, even frontier LLMs still fabricate results, miss hidden errors, and fail to judge novelty reliably. Studying developments through April 2026, we present an end-to-end analysis of AI across the complete research lifecycle, organized into four epistemological phases: Creation (idea generation, literature review, coding & experiments, tables & figures), Writing (paper writing), Validation (peer review, rebuttal & revision), and Dissemination (posters, slides, videos, social media, project pages, and interactive agents). We identify a sharp, stage-dependent boundary between reliable assistance and unreliable autonomy: AI excels at structured, retrieval-grounded, and tool-mediated tasks, but remains fragile for genuinely novel ideas, research-level experiments, and scientific judgment. Generated ideas often degrade after implementation, research code lags far behind pattern-matching benchmarks, and end-to-end autonomous systems have not yet consistently reached major-venue acceptance standards. We further show that greater automation can obscure rather than eliminate failure modes, making human-governed collaboration the most credible deployment paradigm. Finally, we provide a structured taxonomy, benchmark suite, and tool inventory, cross-stage design principles, and a practitioner-oriented playbook, with resources maintained at our project page. |
cite
@misc{kong2026ai,
title={AI for Auto-Research: Roadmap & User Guide},
author={Lingdong Kong and Xian Sun and Wei Chow and Linfeng Li and Kevin Qinghong Lin and Xuan Billy Zhang and Song Wang and Rong Li and Qing Wu and Wei Gao and Yingshuo Wang and Shaoyuan Xie and Jiachen Liu and Leigang Qu and Shijie Li and Lai Xing Ng and Benoit R. Cottereau and Ziwei Liu and Tat-Seng Chua and Wei Tsang Ooi},
year={2026},
eprint={2605.18661},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.18661}
}