Hanxi Xiao | Searcharxiv

arXiv2026

Agentic Artificial Intelligence in Finance: A Comprehensive Survey

The emergence of agentic artificial intelligence (AI) represents a fundamental transformation in financial markets, characterized by autonomous systems capable of reasoning, planning, and adaptive decision-making with minimal human intervention. This comprehensive survey synthesizes recent advances in agentic AI across multiple dimensions of financial operations, including system architecture, market applications, regulatory frameworks, and systemic implications. We examine how agentic AI differs from traditional algorithmic trading and generative AI through its capacity for goal-oriented autonomy, continuous learning, and multi-agent coordination. Our analysis shows that while agentic AI offers substantial potential for enhanced market efficiency, liquidity provision, and risk management, it also introduces novel challenges related to market stability, regulatory compliance, interpretability, and systemic risk. Through a systematic review of foundational research, technical architectures, market applications, and governance frameworks, this survey provides scholars and practitioners with a structured understanding of how agentic AI is reshaping financial markets and identifies critical research directions for ensuring that these systems enhance both operational efficiency and market resilience.

econ.EM↗

arXiv2024

Improving Data-aware and Parameter-aware Robustness for Continual Learning

The goal of Continual Learning (CL) task is to continuously learn multiple new tasks sequentially while achieving a balance between the plasticity and stability of new and old knowledge. This paper analyzes that this insufficiency arises from the ineffective handling of outliers, leading to abnormal gradients and unexpected model updates. To address this issue, we enhance the data-aware and parameter-aware robustness of CL, proposing a Robust Continual Learning (RCL) method. From the data perspective, we develop a contrastive loss based on the concepts of uniformity and alignment, forming a feature distribution that is more applicable to outliers. From the parameter perspective, we present a forward strategy for worst-case perturbation and apply robust gradient projection to the parameters. The experimental results on three benchmarks show that the proposed method effectively maintains robustness and achieves new state-of-the-art (SOTA) results. The code is available at: https://github.com/HanxiXiao/RCL

cs.LG↗