Ruozhen Yang, Yucheng Jiang, Yueqi Jiang, Priyanka Kargupta, Yunyi Zhang, Jiawei Han
2026
7,687 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
- Structure and Diversity Aware Context Bubble Construction for Enterprise Retrieval Augmented SystemsAmir Khurshid, Abhishek Sehgal20268,281 checkouts
- Alex Dantart, Marco Kóvacs-Navarro20265,822 checkouts
- 20265,221 checkouts
- Wei-Chieh Huang, Weizhi Zhang, Yueqing Liang, Yuanchen Bei, Yankai Chen, Tao Feng, Xinyu Pan, Zhen Tan, Yu Wang, Tianxin Wei, Shanglin Wu, Ruiyao Xu, Liangwei Yang, Rui Yang, Wooseong Yang, Chin-Yuan Yeh, Hanrong Zhang, Haozhen Zhang, Siqi Zhu, Henry Peng Zou, Wanjia Zhao, Song Wang, Wujiang Xu, Zixuan Ke, Zheng Hui, Dawei Li, Yaozu Wu, Langzhou He, Chen Wang, Xiongxiao Xu, Baixiang Huang, Juntao Tan, Shelby Heinecke, Huan Wang, Caiming Xiong, Ahmed A. Metwally, Jun Yan, Chen-Yu Lee, Hanqing Zeng, Yinglong Xia, Xiaokai Wei, Ali Payani, Haitong Ma, Wenya Wang, Chenguang Wang, Yu Zhang, Xin Wang, Yongfeng Zhang, Jiaxuan You, Hanghang Tong, Xiao Luo, Xue Liu, Yizhou Sun, Wei Wang, Julian McAuley, James Zou, Jiawei Han, Philip S. Yu, Kai Shu20267,565 checkouts
- Zixia Jia, Jiaqi Li, Yipeng Kang, Yuxuan Wang, Tong Wu, Quansen Wang, Xiaobo Wang, Shuyi Zhang, Junzhe Shen, Qing Li, Siyuan Qi, Yitao Liang, Di He, Zilong Zheng, Song-Chun Zhu20269,286 checkouts
- Yupeng Huo, Yaxi Lu, Zhong Zhang, Haotian Chen, Yankai Lin20269,304 checkouts
- Fengran Mo, Zhan Su, Yuchen Hui, Jinghan Zhang, Jia Ao Sun, Zheyuan Liu, Chao Zhang, Tetsuya Sakai, Jian-Yun Nie20266,665 checkouts
- Manideep Reddy Chinthareddy20265,623 checkouts
- Rubing Chen, Jian Wang, Wenjie Li, Xiao-Yong Wei, Qing Li20265,449 checkouts
- Giulio Corallo, Paolo Papotti20266,826 checkouts
- Anxin Tian, Yiming Li, Xing Li, Hui-Ling Zhen, Lei Chen, Xianzhi Yu, Zhenhua Dong, Mingxuan Yuan20269,920 checkouts
- Sirui Liang, Pengfei Cao, Jian Zhao, Wenhao Teng, Xiangwen Liao, Jun Zhao, Kang Liu20266,487 checkouts
- Miao Su, Yucan Guo, Zhongni Hou, Long Bai, Zixuan Li, Yufei Zhang, Guojun Yin, Wei Lin, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng20265,668 checkouts
- 20267,948 checkouts
- Manzong Huang, Chenyang Bu, Yi He, Xingrui Zhuo, Xindong Wu20268,623 checkouts
- Hua Ye, Siyuan Chen, Ziqi Zhong, Canran Xiao, Haoliang Zhang, Yuhan Wu, Fei Shen20268,585 checkouts
- Zili Wei, Xiaocui Yang, Yilin Wang, Zihan Wang, Weidong Bao, Shi Feng, Daling Wang, Yifei Zhang20269,815 checkouts
- Yue Zhou, Xiaobo Guo, Belhassen Bayar, Srinivasan H. Sengamedu20267,639 checkouts
- 20268,697 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.