Training LLMs to Predict World Events (Guest Post with Mantic)

This guest post is a technical deep dive on what they have built so far.

The top AI forecasting systems are approaching superforecaster-level accuracy on geopolitics and current affairs.

It’s over (Polymarket, 2026) This is exciting because scalable, automated forecasting could significantly improve the quality of decision-making across the economy and in government.

To date, the most successful recipe in forecasting tournaments has been to combine an off-the-shelf LLM (like Gemini 3 or GPT-5) with forecasting-specific context-gathering.

These models, to our knowledge, have not been explicitly trained for forecasting.

Can we improve the recipe by replacing them with models fine-tuned specifically for forecasting?

We target “judgmental forecasting”: prediction problems that require human-like research and reasoning.

Judgmental forecasting is needed for domains like geopolitics, politics, technology, business, and economic policy, where there often isn’t enough data for a standard statistical approach like time-series extrapolation.

…