# ForecastAgentSearch: Towards a Multi-Expert Agent Search System for Geopolitical Event Forecasting

arXiv: https://arxiv.org/abs/2606.31665 · HTML: https://mlsanity.com/paper/2606.31665/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 7 of 7)

- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Retrieval Models Aren't Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models](https://mlsanity.com/paper/2503.01763/) (2025)
- [ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data](https://mlsanity.com/paper/2005.00792/) (2020)
- [MIRAI: Evaluating LLM Agents for Event Forecasting](https://mlsanity.com/paper/2407.01231/) (2024)
- [Context-aware Event Forecasting via Graph Disentanglement](https://mlsanity.com/paper/2308.06480/) (2023)
- [A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting](https://mlsanity.com/paper/2407.11638/) (2024)
- [ThinkTank-ME: A Multi-Expert Framework for Middle East Event Forecasting](https://mlsanity.com/paper/2601.17065/) (2026)

## Built on by (most cited first, 1 of 1)

- [LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications](https://mlsanity.com/paper/2608.23058/) (2026)

## Abstract (arXiv)

Geopolitical event forecasting is a challenging task, as it requires understanding complex regional contexts, dynamic event signals, and uncertain future outcomes. Recent advances in large language model agents provide new opportunities for building forecasting systems that can reason with diverse sources and expert perspectives. In this paper, we present textitForecastAgentSearch, a preliminary framework that formulates geopolitical event forecasting as a multi-expert agent search problem. Given a forecasting query, the system first analyzes the task context, then searches and ranks relevant expert agents based on their regional knowledge, domain expertise, reliability, and complementarity. The selected agents provide specialized analyses, which are further coordinated to generate a final forecast with explanations and uncertainty awareness. We discuss the key design challenges of agent profiling, expert retrieval, ranking, and multi-agent coordination, and outline possible evaluation protocols for future development. This work aims to provide an initial step toward searchable and reliable agent-based forecasting systems.