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
WiseSpec is a requirements‑driven agent framework designed to improve repository‑level code generation. It automatically builds structured, information‑rich requirements, evaluates their quality via execution‑based tests, and iteratively refines them to better guide code generation. Experiments show WiseSpec outperforms all baselines, achieving an average 13.17% improvement in %Resolved.
By Zhao Tian
arXiv:2606. 05608v1 Announce Type: cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve.
By Zhenfeng Cao
arXiv:2608. 15591v1 Announce Type: new Abstract: Large Language Model (LLM) agents deployed in production environments face a fundamental tension: the agent's behavior is frozen at deployment time, while the business rules and edge cases it must handle continue to evolve.
By Pouya Ghiasnezhad Omran, Michael Zimmermann, Duncan Cambridge, Ashmita Kapoor, Tanya Dixit
arXiv:2606. 05608v2 Announce Type: replace-cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve.
By Zhenfeng Cao
arXiv:2606. 29116v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combine natural language understanding with external services and APIs.
By Yutian Tang, Yuming Zhou, Huaming Chen
arXiv:2608. 02650v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks.
By Zian Zhai, Xingyu Tan, Gaowang Zou, Xiaoyang Wang, Wenjie Zhang
arXiv:2609.32490v2 Announce Type: replace
Abstract: LLM-based multi-agent systems (MASs) have shown strong potential for solving complex tasks, but most assume that task requirements are sufficiently...
By Yuchen Song, Andong Chen, Wenxin Zhu, Muyun Yang, Tiejun Zhao
arXiv:2608. 16181v1 Announce Type: cross Abstract: Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science workflows through natural language.
By Wei-Hao Chen, Weixi Tong, Yuan Tian, Chenglong Wang, Tianyi Zhang
arXiv:2609.01045v1 Announce Type: new
Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities as powerful components in agentic systems, enabling sophisticated reasoning and...
By Enci Zhang, Haofeng Wang, Yuesheng Zhu, Xiaole Cui, Guibo Luo
Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons. Reported benchmark gains often obscure recurring failure modes documented across otherwise unrelated evaluation efforts.
arXiv:2606. 19787v1 Announce Type: new Abstract: Large language models are increasingly deployed as autonomous agents for multi-step tasks in executable environments, yet their ability to perform realistic operations research (OR) work remains unclear.
By Jiajun Li, Mingshu Cai, Yixuan Li, Yu Ding, Ran Hou, Guanyu Nie, Xiongwei Han, Wanyuan Wang