Elamur Research⌕
☰
home / papers / 2026 / AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists

AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists

Paper · 2026 · Junshu Pan, Panzhong Lu, Yixuan Weng et al.

Summary

The authors argue that conference- and journal-based publishing struggles to keep up as LLMs and AI scientist systems sharply increase the number of manuscripts. Reviewers are overloaded, feedback is slow, and AI-generated research has no recognized place to appear. In response, they present AiraXiv, an open-access preprint platform where human and AI scientists both publish and read. Papers improve through repeated cycles of AI review and community feedback rather than a single gatekeeping decision. Humans use a web interface. AI agents connect through a Model Context Protocol (MCP) server. The platform parses and summarizes papers, runs pluggable AI reviewers that produce quality signals, offers retrieval and recommendation, and lets users host small themed conferences. AiraXiv served as the submission system for ICAIS 2025. There, AI review scores tracked human accept/reject decisions moderately well, and AI reports arrived within hours. The work matters because it shows a working, scalable alternative to slow peer review, one that also accepts negative results and AI-authored research.

Key points

  • Four core capabilities:
    • paper understanding (summaries and insight extraction)
    • agent- and model-based AI reviewing
    • retrieval and recommendation
    • user-organized lightweight conferences
  • MCP access for AI scientists: agents can submit and revise papers, read papers, view AI reviews, find related work, and post comments.
  • ICAIS 2025 deployment:
    • The conference cycle shrank from about 9 months to 1.5 months.
    • It served over 200 participants, including 6 Nobel laureates.
    • It included a dedicated AI-scientist track.
  • AI review vs. human decisions:
    • The correlation between AI scores and the humans' ultimate verdicts was Pearson r = 0.43, with p < 0.001.
    • They separated accepted from rejected papers with AUC 0.78.
    • AI-generated submissions scored lower than human-written ones, suggesting no bias toward AI writing.
  • Feedback speed and revision:
    • Full AI review reports took about 10.3 hours on average.
    • About 19.3% of submissions were updated during the conference.
    • Revised versions generally reached higher median AI scores.

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

 Institutions

🎓Zhejiang UniversityHangzhou · China172 works · 12,988 citations🎓Westlake UniversityHangzhou · China45 works · 8,028 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 worksDecision Sciences · 209 worksInformation Systems and Management · 41 works

Cite

Cite as Junshu Pan, Panzhong Lu, Yixuan Weng, QiYao Sun, Fang Guo, Zijie Yang, Qiji Zhou and Yue Zhang. 2026. AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-demo.63

@inproceedings{pan2026airaxiv,
  title = {{AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists}},
  author = {Junshu Pan, Panzhong Lu, Yixuan Weng, QiYao Sun, Fang Guo, Zijie Yang, Qiji Zhou and Yue Zhang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-demo.63},
  url = {https://doi.org/10.18653/v1/2026.acl-demo.63}
}

References

50 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. alphaXiv. 2025. alphaxiv. https://www.alphaxiv.org. Accessed: 2025-04. ↗
  2. Analemma. 2026. Introducing fars — fully automated research system. https://analemma.ai/ blog/introducing-fars. Accessed: 2026-02. ↗
  3. Autoscience. 2025. Carl technical report. Autoscience Blog. find
  4. Ariful Azad and Afeefa Banu. 2024. Publication trends in artificial intelligence conferences: The rise of super prolific authors. arXiv preprint arXiv:2412.07793. — [arXiv:2412.07793](https://arxiv.org/abs/2412.07793) find
  5. Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, and Sung Ju Hwang. 2025. Researchagent: Iterative research idea generation over scientific literature with large language models. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Lingu…
  6. Ahmed Salem BaHammam. 2025. Peer review in the artificial intelligence era: A call for developing responsible integration guidelines. find
  7. Daniil A Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes. 2023. Autonomous chemical research with large language models. Nature, 624(7992):570– 578.
  8. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chri… — Language Models are Few-Shot Learners
  9. Nuo Chen, Moming Duan, Andre Huikai Lin, Qian Wang, Jiaying Wu, and Bingsheng He. 2025. Position: The current ai conference model is unsustainable! diagnosing the crisis of centralized ai conference. arXiv preprint arXiv:2508.04586. — [arXiv:2508.04586](https://arxiv.org/abs/2508.04586) find
  10. Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, and Daniel S. Weld. 2020. Specter: Document-level representation learning using citation-informed transformers. ArXiv, abs/2004.07180.
  11. Mingmeng Geng, Caixi Chen, Yanru Wu, Yao Wan, Pan Zhou, and Dongping Chen. 2025. The impact of large language models in academia: from writing to speaking. In Findings of the Association for Computational Linguistics: ACL 2025, pages 19303–19319. find
  12. Arxiv at 20. Nature, Mark A Hanson, Pablo Gómez Barreiro, Paolo Crosetto, and Dan Brockington. 2024. The strain on scientific publishing. Quantitative Science Studies, 5(4):823– 843. find
  13. Yichen He, Guanhua Huang, Peiyuan Feng, Yuan Lin, Yuchen Zhang, Hang Li, and 1 others. 2025. Pasa: An llm agent for comprehensive academic paper search. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11663–11679.
  14. Yuntong Hu, Zhuofeng Li, Zheng Zhang, Chen Ling, Raasikh Kanjiani, Boxin Zhao, and Liang Zhao. 2024. Hireview: Hierarchical taxonomy-driven automatic literature review generation. find
  15. Intology. 2025. Zochi technical report. Intology Blog. find
  16. Yixing Jiang and Andrew Ng. 2025. Stanford agentic reviewer: Technical overview. https://paperreview.ai/tech-overview. Stanford Machine Learning Group and Landing AI. ↗
  17. Mohamed Khalifa and Mona Albadawy. 2024. Using artificial intelligence in academic writing and research: An essential productivity tool. Computer Methods and Programs in Biomedicine Update, 5:100145. find
  18. Laura L Kiessling, Laura E Fernandez, A Paul Alivisatos, and Paul S Weiss. 2016. Chemrxiv: A chemistry preprint server. find
  19. Keigo Kusumegi, Xinyu Yang, Paul Ginsparg, Mathijs de Vaan, Toby Stuart, and Yian Yin. 2025. Scientific production in the era of large language models. Science, 390(6779):1240–1243. find
  20. LangTaoSha. 2025. Langtaosha preprint server. https://langtaosha.org.cn. Accessed: 2025-11. ↗
  21. Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, Daniel A. McFarland, and James Y. Zou. 2024a. Monitoring ai-modified content at scale: A case study on the impact of chatgpt on ai conference peer reviews. In Internati… find
  22. Weixin Liang, Yaohui Zhang, Zhengxuan Wu, Haley Lepp, Wenlong Ji, Xuandong Zhao, Hancheng Cao, Sheng Liu, Siyu He, Zhi Huang, Diyi Yang, Christopher Potts, Christopher D. Manning, and James Zou. 2025a. Quantifying large language model usage in scientific papers. Nature Human Behaviour, 9(12):2599–26… find
  23. Weixin Liang, Yaohui Zhang, Zhengxuan Wu, Haley Lepp, Wenlong Ji, Xuandong Zhao, Hancheng Cao, Sheng Liu, Siyu He, Zhi Huang, Diyi Yang, Christopher Potts, Christopher D Manning, and James Y. Zou. 2024b. Mapping the increasing use of LLMs in scientific papers. In First Conference on Language Modelin… find
  24. Weixin Liang, Yuhui Zhang, Hancheng Cao, Binglu Wang, Daisy Yi Ding, Xinyu Yang, Kailas Vodrahalli, Siyu He, Daniel Scott Smith, Yian Yin, Daniel A. McFarland, and James Zou. 2024c. Can large language models provide useful feedback on research papers? a large-scale empirical analysis. NEJM AI, 1(8):… find
  25. Xun Liang, Jiawei Yang, Yezhaohui Wang, Chen Tang, Zifan Zheng, Shichao Song, Zehao Lin, Yebin Yang, Simin Niu, Hanyu Wang, Bo Tang, Feiyu Xiong, Keming Mao, and Zhiyu li. 2025b. Surveyx: Academic survey automation via large language models. arXiv preprint arXiv:2502.14776. — [arXiv:2502.14776](https://arxiv.org/abs/2502.14776) find
  26. Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha. 2024. The ai scientist: Towards fully automated open-ended scientific discovery. arXiv preprint arXiv:2408.06292.
  27. Andres M. Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller. 2024. Augmenting large language models with chemistry tools. Nature Machine Intelligence, 6(5):525– 535. find
  28. Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Yuanhong Zheng, Dongsheng Ma, Zirui Tang, Boyu Niu, Ziyang Miao, Hejun Dong, Siyi Qian, Jun… — MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing
  29. OpenAI. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
  30. Yixuan Weng, Minjun Zhu, Guangsheng Bao, Hongbo Zhang, Jindong Wang, Yue Zhang, and Linyi Yang. 2025a. Cycleresearcher: Improving automated research via automated review. In The Thirteenth International Conference on Learning Representations. find
  31. Yixuan Weng, Minjun Zhu, Qiujie Xie, Qiyao Sun, Zhen Lin, Sifan Liu, and Yue Zhang. 2025b. Deepscientist: Advancing frontier-pushing scientific findings progressively. arXiv preprint arXiv:2509.26603. find
  32. Yuan Xin, Yixuan Weng, Minjun Zhu, Ying Ling, Chengwei Qin, Michael Hahn, Michael Backes, Yue Zhang, and Linyi Yang. 2026. Safereview: Defending llm-based review systems against adversarial hidden prompts. arXiv preprint arXiv:2604.26506. — [arXiv:2604.26506](https://arxiv.org/abs/2604.26506) find
  33. Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Shengran Hu, Chris Lu, Jakob Foerster, Jeff Clune, and David Ha. 2025. The ai scientist-v2: Workshop-level automated scientific discovery via agentic tree search. arXiv preprint arXiv:2504.08066.
  34. OpenReview. 2025. Openreview: an open peer review platform for scholarly communication. https://openreview.net. Accessed: 2025-04. ↗
  35. Claire Rawlinson and Theodora Bloom. 2019. New preprint server for medical research. find
  36. Samuel Schmidgall and Michael Moor. 2025. Agentrxiv: Towards collaborative autonomous research. arXiv preprint arXiv:2503.18102. find
  37. Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Zicheng Liu, and Emad Barsoum. 2025. Agent laboratory: Using llm agents as research assistants. arXiv preprint arXiv:2501.04227.
  38. Richard Sever, Ted Roeder, Samantha Hindle, Linda Sussman, Kevin-John Black, Janet Argentine, Wayne Manos, and John R Inglis. 2019. biorxiv: the preprint server for biology. BioRxiv, page 833400. find
  39. Chenyang Shao, Dehao Huang, Yu Li, Keyu Zhao, Weiquan Lin, Yining Zhang, Qingbin Zeng, Zhiyu Chen, Tianxing Li, Yifei Huang, Taozhong Wu, Xinyang Liu, Ruotong Zhao, Mengsheng Zhao, Jiaoyang Li, Xuhua Zhang, Yue Wang, Yuanyi Zhen, Fengli Xu, Yong Li, and Tie-Yan Liu. 2025. Omniscientist: Toward a co-… — [arXiv:2511.16931](https://arxiv.org/abs/2511.16931) find
  40. Xiaofeng Shi, Yuduo Li, Qian Kou, Longbin Yu, Jinxin Xie, and Hua Zhou. 2025. Spar: Scholar paper retrieval with llm-based agents for enhanced academic search. arXiv preprint arXiv:2507.15245. — [arXiv:2507.15245](https://arxiv.org/abs/2507.15245) find
  41. Jiabin Tang, Lianghao Xia, Zhonghang Li, and Chao Huang. 2025. Ai-researcher: Autonomous scientific innovation. arXiv preprint arXiv:2505.18705. find
  42. Yidong Wang, Qi Guo, Wenjin Yao, Hongbo Zhang, Xin Zhang, Zhen Wu, Meishan Zhang, Xinyu Dai, Min Zhang, Qingsong Wen, Wei Ye, Shikun Zhang, and Yue Zhang. 2024. Autosurvey: Large language models can automatically write surveys. Advances in neural information processing systems, 37:115119– 115145. find
  43. Xiangchao Yan, Shiyang Feng, Jiakang Yuan, Renqiu Xia, Bin Wang, Lei Bai, and Bo Zhang. 2025. Surveyforge: On the outline heuristics, memory-driven generation, and multi-dimensional evaluation for automated survey writing. In Proceedings of the 63rd Annual Meeting of the Association for Computationa…
  44. Zijie Yang, Yukai Wang, and Lijing Zhang. 2023. Ai becomes a masterbrain scientist. bioRxiv, pages 2023– 04. find
  45. Zijie Yang, Qiji Zhou, Fang Guo, Sijie Zhang, Yexun Xi, Jinglei Nie, Yudian Zhu, Liping Huang, Chou Wu, Yonghe Xia, Xiaoyu Ma, Yingming Pu, Panzhong Lu, Junshu Pan, Mingtao Chen, Tiannan Guo, Yanmei Dou, Hongyu Chen, Anping Zeng, Jiaxing Huang, Tian Xu, and Yue Zhang. 2025. Airalogy: Ai-empowered un… — [arXiv:2506.18586](https://arxiv.org/abs/2506.18586) find
  46. Zonglin Yang, Xinya Du, Junxian Li, Jie Zheng, Soujanya Poria, and Erik Cambria. 2024. Large language models for automated open-domain scientific hypotheses discovery. In Findings of the Association for Computational Linguistics: ACL 2024, pages 13545–13565. find
  47. Hongbo Zhang, Han Cui, Yidong Wang, Yijian Tian, Qi Guo, Cunxiang Wang, Jian Wu, Chiyu Song, and Yue Zhang. 2025a. Deep literature survey automation with an iterative workflow. arXiv preprint arXiv:2510.21900. — [arXiv:2510.21900](https://arxiv.org/abs/2510.21900) find
  48. Pengsong Zhang, Xiang Hu, Guowei Huang, Yang Qi, Heng Zhang, Xiuxu Li, Jiaxing Song, Jiabin Luo, Yijiang Li, Shuo Yin, Chengxiao Dai, Eric Hanchen Jiang, Xiaoyan Zhou, Zhenfei Yin, Boqin Yuan, Jing Dong, Guinan Su, Guanren Qiao, Haiming Tang, Anghong Du, Lili Pan, Zhenzhong Lan, and Xinyu Liu. 2025b… — [arXiv:2508.15126](https://arxiv.org/abs/2508.15126) find
  49. Yunyi Zhang, Ruozhen Yang, Siqi Jiao, SeongKu Kang, and Jiawei Han. 2025c. Scientific paper retrieval with llm-guided semantic-based ranking. arXiv preprint arXiv:2505.21815. find
  50. Minjun Zhu, Yixuan Weng, Linyi Yang, and Yue Zhang. 2025. Deepreview: Improving llm-based paper review with human-like deep thinking process. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 29330–29355. *Figure 5: AiraXiv homepag… find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/pan-2026-airaxiv-ai-driven-open-access/