An LLM-guided tree search system autonomously creates diverse forecasting models that match or beat CDC human-curated ensembles in a 2025-2026 prospective multi-pathogen evaluation.
Ray, Tilmann Gneiting, and Nicholas G
3 Pith papers cite this work. Polarity classification is still indexing.
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A foundation model trained only on disease simulations achieves top-ranked forecasting accuracy across 16 diseases and beats all CDC COVID-19 hub models on early unseen pandemic data.
Prediction markets fail to outperform standard benchmarks for forecasting influenza hospitalizations and measles cases.
citing papers explorer
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Prospective multi-pathogen disease forecasting using autonomous LLM-guided tree search
An LLM-guided tree search system autonomously creates diverse forecasting models that match or beat CDC human-curated ensembles in a 2025-2026 prospective multi-pathogen evaluation.
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Mantis: A Foundation Model for Mechanistic Disease Forecasting
A foundation model trained only on disease simulations achieves top-ranked forecasting accuracy across 16 diseases and beats all CDC COVID-19 hub models on early unseen pandemic data.
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Prediction Markets Underperform Simple Baselines For Infectious Disease Forecasting
Prediction markets fail to outperform standard benchmarks for forecasting influenza hospitalizations and measles cases.