ThinkTank-ME: A Multi-Expert Framework for Middle East Event Forecasting

2026-01-22

AI Summary

This study addresses the limitations of current large language models in forecasting Middle Eastern events, which stem from their monolithic architectures’ inability to capture the region’s intricate historical, cultural, and geopolitical heterogeneity. To overcome this, the authors propose ThinkTank-ME—the first multi-expert collaborative framework specifically designed for Middle Eastern event prediction. Inspired by real-world strategic decision-making processes, the framework simulates expert specialization and coordination to enable fine-grained modeling of multidimensional factors. The work introduces a large language model–based expert specialization system and releases POLECAT-FOR-ME, a new benchmark dataset for Middle Eastern event forecasting. Experimental results demonstrate that ThinkTank-ME significantly outperforms existing single-model approaches on complex spatiotemporal geopolitical prediction tasks, thereby validating the efficacy and novelty of the multi-expert collaboration paradigm.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• event forecasting
• Middle East
• geopolitical nuances
• multi-expert
• LLM limitations

Innovation

• multi-expert framework
• event forecasting
• Middle East geopolitics
• LLM collaboration
• POLECAT-FOR-ME