EvoTS-Agent is a self‑evolving large language model agent designed for autonomous change‑point detection in financial time series. It begins with curated exploratory data analysis to set up candidate models, then iteratively refines detection pipelines using three operators—Revision, Alternative Strategy, and Recombination—guided by validation feedback. Across four benchmark datasets, EvoTS-Agent consistently outperforms existing LLM‑based agents and achieves a 100% execution success rate with all tested backbone LLMs.
EvolveTrade is a self‑evolving framework that treats the system prompt of a tool‑using LLM trading agent as a text‑parameterized policy. After each update interval, a Policy Agent revises this policy using accumulated decision traces and portfolio feedback while keeping the backbone LLM fixed, allowing the agent to refine its information‑acquisition and portfolio‑construction procedures over time. Experiments across multiple market regimes and two LLM backbones show that EvolveTrade often improves Sharpe Ratio and Cumulative Return over fixed‑policy baselines, with behavioral analyses indicating increased code‑mediated analysis and regime‑relevant computations.
By Sehee Kim, Yumin Choi, Minki Kang, Sung Ju Hwang
Self-evolving agents convert interaction feedback into persistent artifacts, such as memories or skills, which in turn guide subsequent decisions. As these artifacts are iteratively updated throughout...
arXiv:2608. 14270v1 Announce Type: new Abstract: Time series analysis in high-stakes domains relies on recurring data releases, where new observations can alter the evidence base and the validity of later conclusions.
By Qingren Yao, Yaxuan Kong, Yuqi Nie, Yichen Li, Stefan Zohren, Anna Vettoruzzo, Qingsong Wen, Ming Jin, Joaquin Vanschoren
arXiv:2607. 27853v2 Announce Type: replace-cross Abstract: Powered by advances in LLMs and autonomous agents, deep research has become one of the most widely adopted agentic products.
By Yijia Xiao, Rujun Han, Yanfei Chen, Zifeng Wang, Ke Jiang, Zhongying CuiZhu, Vishy Tirumalashetty, Wei Wang, Burak Gokturk, Tomas Pfister, Chen-Yu Lee
arXiv:2607. 11141v1 Announce Type: new Abstract: Large language models (LLMs) based agents are beginning to participate in portfolio construction and market analysis, where decisions must be justified under evolving information and risk constraints.
By Changlun Li, Peixian Ma, Qiqi Duan, Zhenyu Lin, Peineng Wu