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LLMs struggle with dynamic financial reasoning in board game simulations

By PulseAugur Editorial · [1 sources] ·

Researchers have developed FinBoardBench, a new evaluation suite designed to test the dynamic financial reasoning and wealth management capabilities of large language models (LLMs). The suite utilizes three classic board games: Cashflow, Acquire, and Monopoly, to assess skills such as cash flow management, investment forecasting, and negotiation. Experiments with nine advanced LLMs showed that while they possess basic planning abilities, they struggle with complex interactions and dynamic decision-making, often prioritizing asset acquisition over liquidity and becoming vulnerable to financial crises. AI

IMPACT This benchmark could reveal critical limitations in LLMs' real-world financial decision-making, guiding future development towards more robust and adaptable AI agents.

RANK_REASON The cluster describes a new academic paper introducing a novel benchmark for evaluating LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs struggle with dynamic financial reasoning in board game simulations

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The cluster describes a new academic paper introducing a novel benchmark for evaluating LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Xuesi Hu, Peng Wang, Jinpeng Miao, Xilin Tao, Caiwei Li, Yue Ma, Jie He, Qiancheng Zhang, Yuntao Zou, Dagang Li ·

    FinBoardBench: Benchmarking Dynamic Wealth Management and Strategic Financial Reasoning of LLMs via Board Game Simulations

    arXiv:2605.27896v1 Announce Type: new Abstract: Recently, large language models (LLMs) have achieved superior performance in static financial reasoning and simple dynamic trading tasks. However, existing static financial benchmarks are insufficient to assess the dynamic wealth ma…

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