Awesome Agentic Reasoning Papers
This repository organizes research by thematic areas that integrate reasoning with action, including planning, tool use, search, self-evolution through memory and feedback, multi-agent systems, and real-world applications and benchmarks.
📄 Based on the survey: Agentic Reasoning for Large Language Models: A Survey
🔔 News
[01/21/26] 🚀 We have released a comprehensive survey on Agentic Reasoning for Large Language Models! The paper is now available on arxiv. We welcome contributions from the community to help expand and improve our survey 🤗!
📋 Table of Contents
- 🔔 News
- 📋 Table of Contents
- [🌟 Introduction](#-introduction…
Awesome Agentic Reasoning Papers
This repository organizes research by thematic areas that integrate reasoning with action, including planning, tool use, search, self-evolution through memory and feedback, multi-agent systems, and real-world applications and benchmarks.
📄 Based on the survey: Agentic Reasoning for Large Language Models: A Survey
🔔 News
[01/21/26] 🚀 We have released a comprehensive survey on Agentic Reasoning for Large Language Models! The paper is now available on arxiv. We welcome contributions from the community to help expand and improve our survey 🤗!
📋 Table of Contents
🌟 Introduction
Bridging thought and action through autonomous agents that reason, act, and learn via continual interaction with their environments. The goal is to enhance agent capabilities by grounding reasoning in action.
We organize agentic reasoning into three layers, each corresponding to a distinct reasoning paradigm under different environmental dynamics:
🔹 Foundational Reasoning. Core single-agent abilities (planning, tool-use, search) in environments
🔹 Self-Evolving Reasoning. Adaptation through feedback, memory, and learning in dynamic settings
🔹 Collective Reasoning. Multi-agent coordination, role specialization, and collaborative intelligence
Across these layers, we further identify complementary reasoning paradigms defined by their optimization settings.
🔸 In-Context Reasoning. Test-time scaling through structured orchestration and adaptive workflows
🔸 Post-Training Reasoning. Behavior optimization via RL and supervised fine-tuning
🤝 Contributing
This collection is an ongoing effort. We are actively expanding and refining its coverage, and welcome contributions from the community. You can:
- Submit a pull request to add papers or resources
- Open an issue to suggest additional papers or resources
- Email us at twei10@illinois.edu, twli@illinois.edu, liu326@illinois.edu
We regularly update the repository to include new research.
📝 Citation
If you find this repository or paper useful, please consider citing the survey paper:
@article{wei2026agentic,
title={Agentic Reasoning for Large Language Models},
author={Wei, Tianxin and Li, Ting-Wei and Liu, Zhining and Ning, Xuying and Yang, Ze and Zou, Jiaru and Zeng, Zhichen and Qiu, Ruizhong and Lin, Xiao and Fu, Dongqi and Li, Zihao and Ai, Mengting and Zhou, Duo and Bao, Wenxuan and Li, Yunzhe and Li, Gaotang and Qian, Cheng and Wang, Yu and Tang, Xiangru and Xiao, Yin and Fang, Liri and Liu, Hui and Tang, Xianfeng and Zhang, Yuji and Wang, Chi and You, Jiaxuan and Ji, Heng and Tong, Hanghang and He, Jingrui},
journal={arXiv preprint arXiv:2601.12538},
year={2026}
}