Tianyi Hu, Niket Tandon, Akhil Arora
2026
7,826 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
- Yiqun Chen, Erhan Zhang, Tianyi Hu, Shijie Wang, Zixuan Yang, Meizhi Zhong, Xiaochi Wei, Yan Gao, Yi Wu, Yao Hu, Jiaxin Mao20266,495 checkouts
- Zhao Wang, Ziliang Zhao, Zhicheng Dou20269,499 checkouts
- Kaixiang Wang, Yidan Lin, Jiong Lou, Zhaojiacheng Zhou, Bunyod Suvonov, Jie Li20269,742 checkouts
- Yang Zhao, Chengxiao Dai, Yue Xiu, Mengying Kou, Yuliang Zheng, Dusit Niyato20269,735 checkouts
- Wei Wen, Sihang Deng, Tianjun Wei, Keyu Chen, Ruizhi Qiao, Xing Sun20268,608 checkouts
- Jiate Liu, Zebin Chen, Shaobo Qiao, Mingchen Ju, Danting Zhang, Bocheng Han, Shuyue Yu, Xin Shu, Jingling Wu, Dong Wen, Xin Cao, Guanfeng Liu, Zhengyi Yang20265,588 checkouts
- Vishnu Sashank Dorbala, Dinesh Manocha20268,716 checkouts
- Weiquan Huang, Zixuan Wang, Hehai Lin, Sudong Wang, Bo Xu, Qian Li, Beier Zhu, Linyi Yang, Chengwei Qin20267,488 checkouts
- Mahdi Astaraki, Mohammad Arshi Saloot, Ali Shiraee Kasmaee, Hamidreza Mahyar, Soheila Samiee20268,216 checkouts
- Yanming Liu, Xinyue Peng, Zixuan Yan, Yanxin Shen, Wenjie Xu, Yuefeng Huang, Xinyi Wang, Jiannan Cao, Jianwei Yin, Xuhong Zhang20269,925 checkouts
- Lei Wei, Xiao Peng, Xu Dong, Niantao Xie, Bin Wang20267,377 checkouts
- Seonho An, Chaejeong Hyun, Min-Soo Kim20267,802 checkouts
- Zhipeng Song, Yizhi Zhou, Xiangyu Kong, Jiulong Jiao, Xinrui Bao, Xu You, Xueqing Shi, Yuhang Zhou, Heng Qi20268,444 checkouts
- Haotian Chen, Qingqing Long, Siyu Pu, Xiao Luo, Wei Ju, Meng Xiao, Yuanchun Zhou, Jianghua Zhao, Xuezhi Wang20266,164 checkouts
- Yuxin Yang, Gangda Deng, Ömer Faruk Akgül, Nima Chitsazan, Yash Govilkar, Akasha Tigalappanavara, Shi-Xiong Zhang, Sambit Sahu, Viktor Prasanna20267,379 checkouts
- Laura Dietz, Bryan Li, Gabrielle Liu, Jia-Huei Ju, Eugene Yang, Dawn Lawrie, William Walden, James Mayfield20266,719 checkouts
- Tianyi Yang, Nashrah Haque, Vaishnave Jonnalagadda, Yuya Jeremy Ong, Zhehui Chen, Yanzhao Wu, Lei Yu, Divyesh Jadav, Wenqi Wei20266,432 checkouts
- Raquib Bin Yousuf, Shengzhe Xu, Mandar Sharma, Andrew Neeser, Chris Latimer, Naren Ramakrishnan20265,138 checkouts
- Yuejie Li, Ke Yang, Tao Wang, Bolin Chen, Bowen Li, Chengjun Mao20265,408 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.