The paper introduces an AI economist agent that integrates large language models, retrieval‑augmented generation, knowledge graphs, and quantitative models to conduct evidence‑based economic and financial scenario analysis. The framework orchestrates LLM agents to plan analyses, retrieve relevant evidence, and structure economic mechanisms, while registered quantitative models produce numerical outcomes and predefined tests validate intermediate results for inclusion in the final report. Applied to European macro‑financial stress scenarios and bank capital analysis, the empirical study demonstrates the agent’s ability to combine flexible evidence retrieval and scenario construction while maintaining traceability to sources and explicit model calculations.
By Masahiro Kato
arXiv:2609. 03553v1 Announce Type: new Abstract: Policy analysis requires more than predicting whether a proposal will pass: it requires identifying who will be affected, how those actors respond, and what follows.
By Linh Le, Melanie Bui, My Chiffon Nguyen, Zachary Schlosser, David Williams-King
FINESSE is an agent‑based simulation framework that generates synthetic, structured datasets of multiple interdependent financial event streams, such as transactions, payments, account status changes, and policy interventions. Each stream has its own action space, schema, and variable types, and the streams are coupled through agents’ evolving latent states, allowing temporally rich interactions. The accompanying FINESSE‑Bench dataset supports four tasks—balance forecasting, transaction fraud detection, missed payment prediction, and next event prediction—and baseline results are provided using various time‑series and event‑sequence methods.
By Tyler Farnan, Benjamin Eng, Adam Abate, Xirui Hou, Rizal Fathony, Nam H. Nguyen, Senthil Kumar
arXiv:2607. 26588v1 Announce Type: new Abstract: The rapid development of large language models (LLMs) has renewed interest in agent-based modeling (ABM).
By Shaopeng Wei, Yufei Cheng, Wenxi Sun, Yepeng Ding, Yu Zhao, Gang Kou
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
arXiv:2607. 19409v1 Announce Type: new Abstract: Recent advances in large language models have accelerated deployment of agentic systems in operational finance.
By Wolfgang M. Pauli, Sarah Panda, Kidus Admassu, Said Bleik, Ademola Okerinde, Jeremy Reynolds