Agentic Retrievers: State of the Method

Charles F. Vardeman II

Center for Research Computing, University of Notre Dame

2025-01-24

Agentic Retrieval-Augmented Generation

Aditi, S., Abul, E., Saket, K., & Khoei, T. T. (2025). Agentic retrieval-Augmented Generation: A survey on agentic RAG. In arXiv [cs.AI]. arXiv. http://arxiv.org/abs/2501.09136)

Knowledge Augmented Generation (KAG)

Liang, L., Sun, M., Gui, Z., Zhu, Z., Jiang, Z., Zhong, L., Qu, Y., Zhao, P., Bo, Z., Yang, J., Xiong, H., Yuan, L., Xu, J., Wang, Z., Zhang, Z., Zhang, W., Chen, H., Chen, W., & Zhou, J. (2024). KAG: Boosting LLMs in professional domains via Knowledge Augmented Generation. In arXiv [cs.CL]. arXiv. https://arxiv.org/abs/2409.13731

Using Large Reasoning Models in Agentic Systems

Xiaoxi, L., Guanting, D., Jiajie, J., Yuyao, Z., Yujia, Z., Yutao, Z., Peitian, Z., & Zhicheng, D. (2025). Search-o1: Agentic search-enhanced large reasoning models. In arXiv cs.AI. arXiv. http://arxiv.org/abs/2501.05366

Open Weight Reasoning Models

DeepSeek-AI, Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., Zhang, X., Yu, X., Wu, Y., Wu, Z. F., Gou, Z., Shao, Z., Li, Z., Gao, Z., … Zhang, Z. (2025). DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. In arXiv [cs.CL]. arXiv. http://arxiv.org/abs/2501.12948

Using good old fashioned rules for Agentic RAG

Allemang, D., & Sequeda, J. (2025). Increasing the accuracy of LLM question-answering systems with ontologies. In Lecture Notes in Computer Science (pp. 324–339). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-77847-6_18

Agentic User Interfaces

Chirag, S., & Ryen, W. W. (2024). Agents Are Not Enough. In arXiv [cs.AI]. arXiv. http://arxiv.org/abs/2412.16241