While Large Language Models (LLMs) provide semantic flexibility for robotic task planning, their susceptibility to hallucination and logical inconsistency limits their reliability in long-horizon domains. To bridge the gap between unstructured environments and rigorous plan synthesis, we propose DUPLEX, an agentic dual-system neuro-symbolic architecture that strictly confines the LLM to schema-guided information extraction rather than end-to-end planning or code generation. In our framework, a feed-forward Fast System utilizes a lightweight LLM to extract entities, relations etc. from natural language, deterministically mapping them into a Planning Domain Definition Language (PDDL) problem file for a classical symbolic planner. To resolve complex or underspecified scenarios, a Slow System is activated exclusively upon planning failure, leveraging solver diagnostics to drive a high-capacity LLM in iterative reflection and repair. Extensive evaluations across 12 classical and household planning domains demonstrate that DUPLEX significantly outperforms existing end-to-end and hybrid LLM baselines in both success rate and reliability. These results confirm that The key is not to make the LLM plan better, but to restrict the LLM to the part it is good at - structured semantic grounding - and leave logical plan synthesis to a symbolic planner.
Towards Urban Planing AI Agent in the Age of Agentic AI
University of Kansas（堪萨斯大学）；University of Florida（佛罗里达大学）；University of Central Florida（中央佛罗里达大学）；University of Alabama（阿拉巴马大学）；Arizona State University（亚利桑那州立大学）
Enhancing Demand-Oriented Regionalization with Agentic AI and Local Heterogeneous Data for Adaptation Planning
College of Design, Construction and Planning, University of Florida（佛罗里达大学设计、建设与规划学院）；Department of Electrical and Computer Engineering, University of Florida（佛罗里达大学电气与计算机工程系）；Florida Institute for Built Environment Resilience (FIBER), University of Florida（佛罗里达大学环境韧性研究所）
LARC: Towards Human-level Constrained Retrosynthesis Planning through an Agentic Framework
Department of Computer Science and Engineering, 2 Division of Medicinal Chemistry & Pharmacognosy, 3 Department of Biomedical Informatics, 4 Translational Data Analytics Institute（1 计算机科学与工程系，2 药物化学与药理学系，3 生物医学信息学系，4 转化数据分析研究所）
CHIME: Credit-Aware Hierarchical Memory Evolution for Long-Horizon Agentic Planning
Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能学院）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism（文化和旅游部闽台非物质文化遗产数字化保护与智能处理重点实验室（厦门大学））；Zhejiang University（浙江大学）；Alibaba Group（阿里巴巴集团）
Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AI
Institute for Clarity in Documentation（清晰文档研究所）；Inria Paris-Rocquencourt（巴黎- Rocquencourt 国家信息与自动化所）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒尔研究实验室）；Beijing Jiaotong University（北京交通大学）
Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling
State Key Laboratory of Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；AMAP, Alibaba Group（阿里集团AMAP）
FinAgent: An Agentic AI Framework Integrating Personal Finance and Nutrition Planning
Faculty of Computer and Information System, Islamic University of Madinah, Saudi Arabia（麦加伊斯兰大学计算机与信息学院）；Department of Computer Science, Effat College of Engineering, Effat University, Saudi Arabia（Effat大学工程学院计算机科学系）；Arab Open University, Bahrain（巴林开放大学）
