Yu Liu, Wenxiao Zhang, Cong Cao, Wenxuan Lu, Fangfang Yuan, Diandian Guo, Kun Peng, Qiang Sun, Kaiyan Zhang, Yanbing Liu, Jin B. Hong, Bowen Zhou, Zhiyuan Ma
2026
8,807 checkouts
AI Agent Papers 2026
The list of papers in AI agent 2026 that engineers, researchers should not miss
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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.
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- Muzhao Tian, Zisu Huang, Xiaohua Wang, Jingwen Xu, Zhengkang Guo, Qi Qian, Yuanzhe Shen, Kaitao Song, Jiakang Yuan, Changze Lv, Xiaoqing Zheng20268,899 checkouts
- Chengyuan Yang, Zequn Sun, Wei Wei, Wei Hu20266,056 checkouts
- Dehao Tao, Guoliang Ma, Yongfeng Huang, Minghu Jiang20268,044 checkouts
- Dongming Jiang, Yi Li, Guanpeng Li, Bingzhe Li20265,894 checkouts
- Hailong Li, Feifei Li, Wenhui Que, Xingyu Fan20266,017 checkouts
- Jiaqi Liu, Yaofeng Su, Peng Xia, Siwei Han, Zeyu Zheng, Cihang Xie, Mingyu Ding, Huaxiu Yao20265,697 checkouts
- Sindhuja Chaduvula, Jessee Ho, Kina Kim, Aravind Narayanan, Mahshid Alinoori, Muskan Garg, Dhanesh Ramachandram, Shaina Raza20265,431 checkouts
- Jean Kaddour, Srijan Patel, Gbètondji Dovonon, Leo Richter, Pasquale Minervini, Matt J. Kusner20269,956 checkouts
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- Lanbo Lin, Jiayao Liu, Tianyuan Yang, Li Cai, Yuanwu Xu, Lei Wei, Sicong Xie, Guannan Zhang20269,508 checkouts
- Minjeong Ban, Jeonghwan Choi, Hyangsuk Min, Nicole Hee-Yeon Kim, Minseok Kim, Jae-Gil Lee, Hwanjun Song20265,041 checkouts
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- Stephen Pilli, Vivek Nallur20268,150 checkouts
- Shahin Honarvar, Amber Gorzynski, James Lee-Jones, Harry Coppock, Marek Rei, Joseph Ryan, Alastair F. Donaldson20265,233 checkouts
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- Tiantian Chen, Jiaqi Lu, Ying Shen, Lin Zhang20268,144 checkouts
- Xuan Liu, Haoyang Shang, Zizhang Liu, Xinyan Liu, Yunze Xiao, Yiwen Tu, Haojian Jin20267,456 checkouts
- Amanda Dsouza, Ramya Ramakrishnan, Charles Dickens, Bhavishya Pohani, Christopher M Glaze20266,576 checkouts
- Roham Koohestani, Ateş Görpelioğlu, Egor Klimov, Burcu Kulahcioglu Ozkan, Maliheh Izadi20269,084 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.