Ruizhe Zhang, Xinke Jiang, Zhibang Yang, Zhixin Zhang, Jiaran Gao, Yuzhen Xiao, Hongbin Lai, Xu Chu, Junfeng Zhao, Yasha Wang
2026
6,054 checkouts
AI Agent Papers 2026
The list of papers in AI agent 2026 that engineers, researchers should not miss
Grok this topic
Follow this list after sign-in.
A handpicked collection of 2026 research papers sourced from arXiv, focused on the core pillars of the AI agent ecosystem—multi-agent coordination, memory and RAG, tooling, evaluation and observability, and security. Whether you're an AI engineer building agentic systems, a researcher exploring emerging architectures, or a developer integrating LLM agents into real-world products, this collection keeps you current on what’s working, what’s failing, and where the field is headed. Updated weekly from arXiv.
Managed by Attendemia
- Percy Jardine20267,097 checkouts
- Zhenghao Li, Zhi Zheng, Wei Chen, Jielun Zhao, Yong Chen, Tong Xu, Enhong Chen20265,721 checkouts
- Xiaochen Zhu, Caiqi Zhang, Yizhou Chi, Tom Stafford, Nigel Collier, Andreas Vlachos20267,213 checkouts
- Jingbo Wang, Sendong Zhao, Jiatong Liu, Haochun Wang, Wanting Li, Bing Qin, Ting Liu20265,796 checkouts
- Junhyuk Choi, Jeongyoun Kwon, Heeju Kim, Haeun Cho, Hayeong Jung, Sehee Min, Bugeun Kim20265,964 checkouts
- 20269,648 checkouts
- Zhilun Zhou, Zihan Liu, Jiahe Liu, Qingyu Shao, Yihan Wang, Kun Shao, Depeng Jin, Fengli Xu20267,716 checkouts
- Jiuzhou Zhao, Chunrong Chen, Chenqi Qiao, Lebin Zheng, Minqi Han, Yanchi Liu Yongzhou Xu Xiaochuan Xu Min Zhang20269,822 checkouts
- Alessio Buscemi, Daniele Proverbio, Alessandro Di Stefano, The Anh Han, German Castignani, Pietro Liò20268,207 checkouts
- 20268,087 checkouts
- Haozhen Zhang, Haodong Yue, Tao Feng, Quanyu Long, Jianzhu Bao, Bowen Jin, Weizhi Zhang, Xiao Li, Jiaxuan You, Chengwei Qin, Wenya Wang20269,648 checkouts
- Joseph Fioresi, Parth Parag Kulkarni, Ashmal Vayani, Song Wang, Mubarak Shah20265,051 checkouts
- Hao Yang, Zhiyu Yang, Xupeng Zhang, Wei Wei, Yunjie Zhang, Lin Yang20268,345 checkouts
- Taoye Yin, Haoyuan Hu, Yaxin Fan, Xinhao Chen, Xinya Wu, Kai Deng, Kezun Zhang, Feng Wang20266,956 checkouts
- Chang Yang, Chuang Zhou, Yilin Xiao, Su Dong, Luyao Zhuang, Yujing Zhang, Zhu Wang, Zijin Hong, Zheng Yuan, Zhishang Xiang, Shengyuan Chen, Huachi Zhou, Qinggang Zhang, Ninghao Liu, Jinsong Su, Xinrun Wang, Yi Chang, Xiao Huang20268,426 checkouts
- Konosuke Yoshizato, Kazuma Shimizu, Ryota Higa, Takanobu Otsuka20266,885 checkouts
- Liangtao Lin, Zhaomeng Zhu, Tianwei Zhang, Yonggang Wen20265,789 checkouts
- Qirui Mi, Zhijian Ma, Mengyue Yang, Haoxuan Li, Yisen Wang, Haifeng Zhang, Jun Wang20267,128 checkouts
- Haojia Zhu, Qinyuan Xu, Haoyu Li, Yuxi Liu, Hanchen Qiu, Jiaoyan Chen, Jiahui Jin20265,967 checkouts
FAQ
What is AI Agent Papers 2026?
AI Agent Papers 2026 is a curated collection of research papers focused on the AI agent ecosystem. It brings together 2026 papers sourced from arXiv so readers can quickly discover important work without searching across fragmented sources. The list is designed to make high-signal agent research easier to browse, compare, and revisit.
What topics does AI Agent Papers 2026 cover?
The collection covers core topics in the AI agent ecosystem, including multi-agent coordination, memory and RAG, tooling, evaluation and observability, and security. These themes reflect the practical and research challenges involved in building reliable LLM agent systems. The page is intended to help readers follow the most relevant directions in agentic AI.
Where do the papers in AI Agent Papers 2026 come from?
The papers in this collection are sourced from arXiv. This makes the list especially useful for readers who want early access to current research in agentic AI and related systems work. It serves as a streamlined entry point into fast-moving preprint literature.
Who should read AI Agent Papers 2026?
This list is useful for AI engineers, researchers, and developers working with LLM agents and agentic systems. It is especially helpful for people building multi-agent workflows, retrieval-augmented systems, evaluation pipelines, and secure AI products. Anyone trying to stay current with practical and emerging agent research can benefit from it.
How often is AI Agent Papers 2026 updated?
AI Agent Papers 2026 is updated weekly from arXiv. Regular updates help readers keep up with new papers, shifting trends, and emerging techniques in the AI agent ecosystem. This makes the collection a useful ongoing resource rather than a static reading list.
Why use a curated list of AI agent papers instead of searching arXiv directly?
A curated list saves time by filtering out noise and highlighting papers that are more likely to matter for real-world learning and implementation. Instead of manually scanning large numbers of preprints, readers can start with a focused shortlist organized around meaningful themes. This is especially valuable in agentic AI, where new papers appear quickly and terminology changes fast.
Does AI Agent Papers 2026 focus on research only, or also on practical AI engineering?
The collection is research-driven, but it is also highly relevant to practical AI engineering. Its topic coverage includes areas such as tooling, observability, evaluation, memory, and security, which are central to production-grade agent systems. That makes it useful not just for academic reading, but also for engineering decision-making.
How can I use AI Agent Papers 2026 effectively?
A good way to use the list is to start with the topics most relevant to your current work, such as multi-agent coordination or memory and RAG. From there, you can track recurring methods, compare evaluation approaches, and identify papers that influence current agent design patterns. Over time, the list can function as both a discovery tool and a structured.
What makes AI Agent Papers 2026 useful for tracking trends in agentic AI?
Because the list is focused specifically on 2026 papers and refreshed weekly, it helps readers spot emerging patterns earlier than broader evergreen resources. It also narrows attention to core agent-system concerns like coordination, retrieval, tooling, observability, and security. That combination makes it useful for trend tracking as well as technical study.
Is AI Agent Papers 2026 suitable for beginners?
Yes, the list can be useful for beginners, especially those who want a structured starting point in agentic AI research. While some papers may be advanced, the curated format makes it easier to identify important themes and gradually build understanding. Beginners can start with familiar topics like RAG or agent tooling before moving into more complex multi-agent and evaluation work.