Recent advancements in large language models (LLMs) and agentic systems have shown exceptional decision-making capabilities, revealing significant potential for autonomic finance. Current financial trading agents predominantly simulate anthropomorphic roles that inadvertently introduce emotional biases and rely on peripheral information, while being constrained by the necessity for continuous inference during deployment. In this paper, we pioneer the harmonization of strategic depth in agents with the mechanical rationality essential for quantitative trading. Consequently, we present TiMi (Trade in Minutes), a rationality-driven multi-agent system that architecturally decouples strategy development from minute-level deployment. TiMi leverages specialized LLM capabilities of semantic analysis, code programming, and mathematical reasoning within a comprehensive policy-optimization-deployment chain. Specifically, we propose a two-tier analytical paradigm from macro patterns to micro customization, layered programming design for trading bot implementation, and closed-loop optimization driven by mathematical reflection. Extensive evaluations across 200+ trading pairs in stock and cryptocurrency markets empirically validate the efficacy of TiMi in stable profitability, action efficiency, and risk control under volatile market dynamics.
Can Agentic Trading Systems Pay for Their Own Intelligence?
HKUST(GZ)（香港科技大学（广州））；Paradoox AI（悖论人工智能公司）；E Fund Management Co., Ltd（易方达基金管理有限公司）；MBZUAI（Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学））；The University of Tokyo（东京大学）
Orchestration Framework for Financial Agents: From Algorithmic Trading to Agentic Trading
SecureFinAI Lab, Columbia University（安全金融人工智能实验室，哥伦比亚大学）；Purdue University（普渡大学）；Rensselaer Polytechnic Institute（拉特格斯理工学院）；Stevens Institute of Technology（史蒂文斯理工学院）
Agentic Trading: When LLM Agents Meet Financial Markets
College of Electronic and Information Engineering, Shenzhen University, Shenzhen, China（深圳大学电子与信息工程学院）；Shenzhen Audencia Financial Technology Institute, Shenzhen University, Guangdong, People's Republic of China（深圳大学审计金融科技研究所）
An Agentic AI Control Plane for 6G Network Slice Orchestration, Monitoring, and Trading
Florida International University, USA（佛罗里达国际大学）；Center for Wireless Communications, University of Oulu, Finland（无线通信中心，奥卢大学）；Accenture Technology Labs, Arlington, VA, USA（埃森哲技术实验室）；Nanyang Technological University, Singapore（南洋理工大学）
FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading
Harvard University（哈佛大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Columbia University（哥伦比亚大学）；University of Minnesota（明尼苏达大学）；NVIDIA（英伟达公司）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；TheFinAI；University of Manchester（曼彻斯特大学）
Herculean: An Agentic Benchmark for Financial Intelligence
The Fin AI；Yale University（耶鲁大学）；Columbia University（哥伦比亚大学）；Stevens Institute of Technology（史蒂文斯理工学院）；NVIDIA（英伟达）；New York University（纽约大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Florida（佛罗里达大学）；MBZUAI；Université de Montréal（蒙特利尔大学）；University of Minnesota（明尼苏达大学）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）；National Institute of Advanced Industrial Science and Technology（国家先进工业科学与技术研究院）；University of Liverpool（利物浦大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；National University of Singapore（新加坡国立大学）；Halmstad University（哈尔姆斯塔德大学）；University of Manchester（曼彻斯特大学）；Cardiff University（卡迪夫大学）；McGill University（麦吉尔大学）；Mila – Quebec AI Institute（魁北克人工智能研究所）
Poisoning Agentic Alpha: Adversarial Vulnerabilities Across Roles and Architectures in Multi-Agent Trading Systems
Sungkyunkwan University（成均馆大学）；University College London（伦敦大学学院）；University of Edinburgh（爱丁堡大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；BlackRock, Inc.（贝莱德集团）；Google（谷歌公司）；University of Florida（佛罗里达大学）；LinqAlpha；UNIST（蔚山国家科学技术研究院）
Active Inference: A method for Phenotyping Agency in AI systems?
Independent Researcher Laboratoire d'Analyse Cognitive de l'Information, Universit\' e du Qu\' e bec \` a Montr\' e al, Qu\' e bec, Canada Centre of Excellence for AI；Robotics, Sheffield Hallam University, Sheffield, UK Methods；Statistical Computing, Max Planck Institute for Human Cognitive；Brain Sciences, Leipzig, Germany Department of Computer Science, School of Physics, Engineering；Computer Science, University of Hertfordshire, Hatfield, UK Wellcome Centre for Human Neuroimaging, University College London, London, UK Department of Engineering；Informatics, University of Sussex, Falmer, Brighton, BN1 9RH, UK
HAG: Hierarchical Demographic Tree-based Agent Generation for Topic-Adaptive Simulation
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学前沿交叉科学学院）
