Coliseo
Historically, quantitative models are domain specific. Brilliant people spend their best years testing features, tuning hyperparameters, and iterating architectures within a narrow domain. But scale is the panacea: large models will find patterns people, and specialized models, could not. Forecasting generalizes. Zoa trains cross-domain event forecasting engines. *Automating Iteration* LLMs—embedded in multi-agent optimization loops and evaluated against fixed policies—can automate the build-test-improve modeling cycle. Think AlphaEvolve for forecasting problems. *Sample-Efficient General Models* Today’s forecasting models are narrowly crafted with deep human priors. But larger models will outperform state-of-the-art specialized models. Unlike existing event models, our models leverage data from across contexts and rely less on human intuition. And compared to LLMs, o
Ratings are live Elo scores from head-to-head crowd votes — opinion, not fact or financial advice. How it works →