Zijie Yang
Author directoryOther people with similar names: Zijie Yang
2026
How Far Are AI Scientists from Changing the World? A Survey of Autonomous Scientific Discovery
Qiujie Xie | Yixuan Weng | Minjun Zhu | Junying Fang | Fuchen Shen | Shulin Huang | Zhen Lin | Zhouyang Wang | Shichen Li | Jiahui Zhou | Zilan Mao | Sifan Liu | Zhenhao Liu | Dalv Yin | Fanjunduo Wei | Zhiyuan Ning | Zijie Yang | Linyi Yang | Jian Wu | Yue Zhang
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Qiujie Xie | Yixuan Weng | Minjun Zhu | Junying Fang | Fuchen Shen | Shulin Huang | Zhen Lin | Zhouyang Wang | Shichen Li | Jiahui Zhou | Zilan Mao | Sifan Liu | Zhenhao Liu | Dalv Yin | Fanjunduo Wei | Zhiyuan Ning | Zijie Yang | Linyi Yang | Jian Wu | Yue Zhang
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
This survey asks how far AI Scientist systems are from changing the world, and argues that their impact should be evaluated not by whether they produce plausible papers, but by whether they produce auditable research trajectories that can be inspected, reproduced, and challenged. We first provide a definition of AI Scientists, distinguish them from adjacent scientific AI systems, and organize the field around four core capabilities: knowledge acquisition, hypothesis generation, experimental validation, and evolution. We further synthesize representative systems by autonomy, execution grounding, auditability, human control, and evidence level. The resulting picture shows a field moving from prompt-to-paper automation toward auditable research laboratories, where executable grounding, provenance, and human-governed autonomy become central. We conclude by identifying open challenges toward trustworthy AI-driven science and providing a roadmap for integrating AI Scientists into the future landscape of scientific discovery.
AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists
Junshu Pan | Panzhong Lu | Yixuan Weng | QiYao Sun | Fang Guo | Zijie Yang | Qiji Zhou | Yue Zhang
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Junshu Pan | Panzhong Lu | Yixuan Weng | QiYao Sun | Fang Guo | Zijie Yang | Qiji Zhou | Yue Zhang
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Recent advances in artificial intelligence (AI) have accelerated the growth of both human-authored and AI-generated research outputs, placing increasing strain on traditional academic publishing systems and challenging the scalability of conference- and journal-centered paradigms amid rising submission volumes, reviewer workload, and venue size. To address these challenges, we explore an AI-era publishing paradigm in which both human and AI scientists participate as authors and readers, and papers evolve through continuous, feedback-driven iteration. We propose AiraXiv, an AI-driven open-access platform built on open preprints, AI-augmented analysis and review, and reader feedback. AiraXiv supports human scientists through an interactive UI and AI scientists through Model Context Protocol (MCP)-based interactions. We validate AiraXiv through real-world deployments, including serving as the submission platform for ICAIS 2025, demonstrating its potential as a fast, inclusive, and scalable research infrastructure for the AI era. AiraXiv is publicly available at https://airaxiv.com.