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AI for Auto-Research: Roadmap & User Guide

Paper · 2026 · Lingdong Kong, Xian Sun, Wei Chow et al.

Summary

This roadmap examines how far AI can be trusted at each step of producing science, from the first idea to public outreach. The authors review work published up to June 2026 and organize it into a lifecycle of four phases (Creation, Writing, Validation, Dissemination), split into eight stages. These stages include figure and table generation, rebuttal and revision, and turning papers into posters, slides, videos and interactive agents.

Their central finding is that the line between dependable help and risky autonomy depends on the stage. AI does well on tasks that are structured, grounded in retrieval, or carried out through tools. It struggles with genuinely new ideas, research-grade experiments and scientific judgment. Promising ideas often weaken once implemented, research code trails benchmark scores, and fully autonomous pipelines still miss top-venue standards. More automation can hide errors rather than remove them.

The paper therefore recommends human-governed collaboration. It also supplies a taxonomy, benchmark collection, tool inventory, design principles and a practitioner playbook, which makes it useful for any team adopting AI research assistants.

Key points

  • Fully automated systems can already produce a research paper for roughly $15, yet frontier LLMs still fabricate results and judge novelty poorly.
  • The framework has 4 phases and 8 stages (S1 Idea Generation through S8 Dissemination). It covers all 8 stages, while five comparable surveys (e.g., AI4Research, LLM4SR) cover between 1 and 5.
  • S7 (Rebuttal & Revision) and S8 (Dissemination) are introduced as new stages. The review–response loop is modeled explicitly, so it can send work back to experiments, figures or writing.
  • Cataloged systems include Google Co-Scientist, R&D-Agent, ResearchTown, AgentRxiv (11.4% gain on MATH-500) and LabClaw (206 biomedical skills), plus lifecycle benchmarks such as ResearchClawBench.
  • Resources are maintained at worldbench.github.io/awesome-ai-auto-research and the GitHub repo worldbench/awesome-ai-auto-research.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksArts and Humanities · 17,959 worksSocial Sciences · 2,009 worksSafety Research · 665 worksHistory and Philosophy of Science · 83 works

Cite

Cite as 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 and Wei Tsang Ooi. 2026. AI for Auto-Research: Roadmap & User Guide. arXiv:2605.18661.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2605.18661 · arXiv 2605.18661

@misc{kong2026ai,
  title = {{AI for Auto-Research: Roadmap \& User Guide}},
  author = {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 and Wei Tsang Ooi},
  year = {2026},
  doi = {10.48550/arxiv.2605.18661},
  eprint = {2605.18661},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2605.18661}
}

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