AI for Auto-Research: Roadmap & User Guide
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| author | Kong, Lingdong Sun, Xian Chow, Wei Li, Linfeng Lin, Kevin Qinghong Zhang, Xuan Billy Wang, Song Li, Rong Wu, Qing Gao, Wei Wang, Yingshuo Xie, Shaoyuan Liu, Jiachen Qu, Leigang Li, Shijie Ng, Lai Xing Cottereau, Benoit R. Liu, Ziwei Chua, Tat-Seng Ooi, Wei Tsang |
| author_facet | Kong, Lingdong Sun, Xian Chow, Wei Li, Linfeng Lin, Kevin Qinghong Zhang, Xuan Billy Wang, Song Li, Rong Wu, Qing Gao, Wei Wang, Yingshuo Xie, Shaoyuan Liu, Jiachen Qu, Leigang Li, Shijie Ng, Lai Xing Cottereau, Benoit R. Liu, Ziwei Chua, Tat-Seng Ooi, Wei Tsang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18661 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI for Auto-Research: Roadmap & User Guide Kong, Lingdong Sun, Xian Chow, Wei Li, Linfeng Lin, Kevin Qinghong Zhang, Xuan Billy Wang, Song Li, Rong Wu, Qing Gao, Wei Wang, Yingshuo Xie, Shaoyuan Liu, Jiachen Qu, Leigang Li, Shijie Ng, Lai Xing Cottereau, Benoit R. Liu, Ziwei Chua, Tat-Seng Ooi, Wei Tsang Artificial Intelligence 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. |
| title | AI for Auto-Research: Roadmap & User Guide |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.18661 |