Public articles linked to the same research event.
arXiv EpiWorld is a closed-loop framework that grounds an LLM policy actor in a learned action-conditioned epidemiological world model and a tiered skill library of public-health protocols, surveillance tools, and lessons accumulated through after-action analysis; the world model predicts regional epidemic evolution and enables fast counterfactual rollouts that feed policy selection and refinement, and on retrospective COVID-19 and Influenza datasets it achieves the best out-of-distribution Peak-MAE among forecasting baselines while the closed-loop framework reduces cumulative hospitalisation by up to 59% across datasets and by an average of about 16% across six LLM backbones, outperforming reinforcement-learning and optimal-control policy baselines.
EpiWorld is a closed-loop framework that grounds an LLM policy actor in a learned action-conditioned epidemiological world model and a tiered skill library of public-health protocols, surveillance tools, and lessons accumulated through after-action analysis; the world model predicts regional epidemic evolution and enables fast counterfactual rollouts that feed policy selection and refinement, and on retrospective COVID-19 and Influenza datasets it achieves the best out-of-distribution Peak-MAE among forecasting baselines while the closed-loop framework reduces cumulative hospitalisation by up to 59% across datasets and by an average of about 16% across six LLM backbones, outperforming reinforcement-learning and optimal-control policy baselines.
EpiWorld is a closed-loop framework that grounds an LLM policy actor in a learned action-conditioned epidemiological world model and a tiered skill library of public-health protocols, surveillance tools, and lessons accumulated through after-action analysis; the world model predicts regional epidemic evolution and enables fast counterfactual rollouts that feed policy selection and refinement, and on retrospective COVID-19 and Influenza datasets it achieves the best out-of-distribution Peak-MAE among forecasting baselines while the closed-loop framework reduces cumulative hospitalisation by up to 59% across datasets and by an average of about 16% across six LLM backbones, outperforming reinforcement-learning and optimal-control policy baselines.
EpiWorld is a closed-loop framework that grounds an LLM policy actor in a learned action-conditioned epidemiological world model and a tiered skill library of public-health protocols, surveillance tools, and lessons accumulated through after-action analysis; the world model predicts regional epidemic evolution and enables fast counterfactual rollouts that feed policy selection and refinement, and on retrospective COVID-19 and Influenza datasets it achieves the best out-of-distribution Peak-MAE among forecasting baselines while the closed-loop framework reduces cumulative hospitalisation by up to 59% across datasets and by an average of about 16% across six LLM backbones, outperforming reinforcement-learning and optimal-control policy baselines.