Event forecasting is inherently influenced by multifaceted considerations, including international relations, regional historical dynamics, and cultural contexts. However, existing LLM-based approaches employ single-model architectures that generate predictions along a singular explicit trajectory, constraining their ability to capture diverse geopolitical nuances across complex regional contexts. To address this limitation, we introduce ThinkTank-ME, a novel Think Tank framework for Middle East event forecasting that emulates collaborative expert analysis in real-world strategic decision-making. To facilitate expert specialization and rigorous evaluation, we construct POLECAT-FOR-ME, a Middle East-focused event forecasting benchmark. Experimental results demonstrate the superiority of multi-expert collaboration in handling complex temporal geopolitical forecasting tasks. The code is available at https://github.com/LuminosityX/ThinkTank-ME.

翻译：事件预测本质上受到多方面因素的影响，包括国际关系、区域历史动态和文化背景。然而，现有的基于大语言模型的方法采用单一模型架构，沿着单一的显式轨迹生成预测，这限制了其捕捉复杂区域背景下多样化地缘政治细微差别的能力。为解决这一局限，我们提出了ThinkTank-ME，一个用于中东事件预测的新型智库框架，旨在模拟现实世界战略决策中的协作专家分析。为促进专家专业化和严格评估，我们构建了POLECAT-FOR-ME，一个专注于中东的事件预测基准。实验结果表明，多专家协作在处理复杂的时序地缘政治预测任务方面具有优越性。代码可在 https://github.com/LuminosityX/ThinkTank-ME 获取。