ThinkTank-ME: A Multi-Expert Framework for Middle East Event Forecasting
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911395603283968 |
|---|---|
| author | Li, Haoxuan Chang, He Ma, Yunshan Bin, Yi Yang, Yang Ng, See-Kiong Chua, Tat-Seng |
| author_facet | Li, Haoxuan Chang, He Ma, Yunshan Bin, Yi Yang, Yang Ng, See-Kiong Chua, Tat-Seng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17065 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ThinkTank-ME: A Multi-Expert Framework for Middle East Event Forecasting Li, Haoxuan Chang, He Ma, Yunshan Bin, Yi Yang, Yang Ng, See-Kiong Chua, Tat-Seng Machine Learning Artificial Intelligence Computation and Language Computers and Society Multiagent Systems 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. |
| title | ThinkTank-ME: A Multi-Expert Framework for Middle East Event Forecasting |
| topic | Machine Learning Artificial Intelligence Computation and Language Computers and Society Multiagent Systems |
| url | https://arxiv.org/abs/2601.17065 |