The article surveys LLM-based agentic reasoning frameworks, presenting a unified formal language that categorizes them into single-agent, tool-based, and multi-agent methods. It reviews application scenarios in scientific discovery, healthcare, software engineering, society, economics, and general-purpose tasks, and compares the distinct features and evaluation strategies of each category. The survey highlights the rapid development of complex agentic systems in real-world contexts.
By Bingxi Zhao, Lin Geng Foo, Ping Hu, Christian Theobalt, Hossein Rahmani, Jun Liu
arXiv:2604.13706v2 Announce Type: replace
Abstract: Professional fact-checkers rely on domain knowledge and deep contextual understanding to verify claims. Large language models (LLMs) and large reas...
By Dhruv Sahnan, Subhabrata Dutta, Tanmoy Chakraborty, Preslav Nakov, Iryna Gurevych
arXiv:2608.29263v1 Announce Type: new
Abstract: Large Language Models (LLMs) often suffer from hallucination and struggle with complex reasoning tasks requiring multi-hop domain knowledge. While inte...
By Yuwei Lou, Hao Hu, Yuzhou Jiang, Zongfei Zhang, Liang Wang, Jincai Liu, Jidong Ge, Xianping Tao
arXiv:2610.28578v1 Announce Type: new
Abstract: In this report, we introduce Sage-A1.5, a 35B Mixture-of-Experts agentic model that builds on A1 to advance long-horizon problem solving through adapti...
By Wenjie Lou, Tianshuo Peng, Shiyang Feng, Shengji Tang, Runmin Ma, Ruizhe Chen, Jinxin Shi, Haoyang Peng, Fangchen Yu, Zhijie Zhong, Yuhao Zhou, Yue Fan, Yixin Chen, Yiming Wu, Yiheng Wang, Yigeng Jiang, Yichen Jiang, Yi Xie, Xu Guo, Xiaohan He, Tao Hu, Songtao Huang, Siyu Zhou, Shuaiyu Zhang, Shangheng Du, Qinglong Cao, Kun Li, Jingqi Ye, Hongbin Zhou, Guohang Yan, Zongsheng Cao, Ziyi Wang, Zhuo Liu, Zhiyin Yu, Zhiwang Zhou, Zelin Tan, Yuqing Zhu, Yaoze Zhang, Yang Chen, Xuming He, Xiuliang Liang, Xingjian Guo, Xiang Zhuang, Weiping Shen, Weida Wang, Tianyuan Zhou, Tianlin Ye, Tengyuan Fan, Siyu Yan, Shuo Li, Shuchen Pu, Shengyuan Xu, Shao Zhang, Ran Wei, Qinyuan Qin, Qingyu Yang, Nianchen Deng, Meng Dai, Kuikun Liu, Kaitao Chen, Juncheng Xiao, Jiangning Liu, Jianbiao Mei, Jia Yu, Haonan He, Hangfan Zhang, Cheng Yang, Chen Zhang, Bihao Zhan, Benteng Cheng, Yuqiang Li, Yihao Liu, Yang Yang, Xujun Fu, Xiaosong Wang, Xiangyu Yue, Wenwei Zhang, Shuyue Hu, Pinlong Cai, Mianxin Liu, Lei Bai, Jie Zhou, Jie Hou, Jiamin Wu, Hui Wang, Haijun Li, Guangyu Wang, Dongzhan Zhou, Chunfeng Song, Chao Yang, Biqing Qi, Xing Xie, Dahua Lin, Bowen Zhou, Xiangchao Yan, Wenlong Zhang, Shufei Zhang, Peng Ye, Bo Zhang
arXiv:2608. 12877v1 Announce Type: new Abstract: Multi-hop fact verification, which verifies claims by reasoning over multiple pieces of evidence, is critical for combating misinformation on social media yet remains highly challenging.
By Runze Zhao, Zixin Tang, Xiaoshuai Hao, Leyuan Chang, Xiaopeng Fu, Boyu Qiao, Dongyang Zhang
MIND is an LLM-driven framework that automates hypothesis validation in materials research by structuring the discovery process into hypothesis refinement, experimentation, and debate-based validation within a multi-agent pipeline. It incorporates Machine Learning Interatomic Potentials, notably SevenNet-Omni, to perform scalable in‑silico experiments and offers a web-based interface for automated hypothesis testing. The modular design allows integration of additional experimental modules, making the framework adaptable to broader scientific workflows.
By Geonhee Ahn, Donghyun Lee, Hayoung Doo, Jonggeol Na, Hyunsoo Cho, Sookyung Kim
arXiv:2607. 14178v1 Announce Type: new Abstract: Recent advances in Large Language Models have fueled autonomous AI agents capable of tackling complex scientific tasks, yet existing automated research systems remain predominantly focused on empirically driven domains with quantitative benchmarks, leaving theory-driven discovery, particularly in mathematically grounded disciplines requiring rigorous proofs and synthesis of domain knowledge, largely underexplored.
By Yutong He, Daibo Li, Guohong Li, Jiahe Geng, Zhengyang Huang, Can Ren, Zekun Zhang, Yifan Liu, Shuchen Zhu, Hengrui Zhang, Boao Kong, Ming Sun, Shu Li, Chenyi Li, Jiang Hu, Kun Yuan, Zaiwen Wen, Pingwen Zhang
arXiv:2609.27297v2 Announce Type: replace
Abstract: Agentic science envisions many autonomous agents investigating concurrently while building on a shared, evolving body of scientific knowledge. This...
By Yuan Huang, Sihan Hu, Hongyu Gu, Chao Ma, Jiaxing Zhang, Zhiyong Zou, Caiyu Fan, Yan Xiao, Mingjun Xu, Chenyu Xie, Mingzhen Ju, Zhehao Ma, Qi Zhang, Baozong Wang, Yu Li, Zhiyuan Yao, Ruoxue Liao, Xinyu Li, Linfeng Zhang, Kun Chen, Weinan E
The paper proposes a user‑centric Chain‑of‑Thought (CoT) reasoning framework that structures LLM reasoning traces into self‑contained, verifiable steps using XML‑like tags. This design allows users to independently assess and correct the AI’s reasoning while preserving performance on mathematical reasoning tasks. User studies show that the approach improves perceived usefulness and ease of use compared to standard CoT.
By Philipp Schr\"oppel
GraphCert introduces a method to bootstrap graph reasoning agents by generating graph‑grounded question‑answer pairs and certifying the supporting evidence. The approach uses a Bootstrapped Graph Quizzer to produce QA pairs, then executes and semantically curates the evidence into certified rubrics that guide reward‑based training of a Graph Solver. Experiments on five GRBENCH domains show GraphCert outperforms larger LLM agents and demonstrates robust policy transfer across heterogeneous graphs.
By Weiqi Jiang, Yuchen Ying, Rui Wang, Kaixuan Chen, Bingde Hu, Shunyu Liu, Yu Wang, Tongya Zheng
arXiv:2510. 27568v2 Announce Type: replace Abstract: Solving mathematical reasoning problems requires not only accurate access to relevant knowledge but also careful, multi-step thinking.
By Ali Asgarov, Umid Suleymanov, Aadyant Khatri
arXiv:2604.16778v3 Announce Type: replace-cross
Abstract: Modern agents specialize in varying domains while there is no clear approach combining different domain skills. We propose a federated learni...
By Dixi Yao, Tahseen Rabbani, Manzil Zaheer, Tian Li