{"slug": "epiworld-grounding-llm-policy-agents-in-epidemiological-world-models", "title": "EpiWorld: Grounding LLM Policy Agents in Epidemiological World Models", "summary": "EpiWorld, 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, reduced cumulative hospitalisation by up to 59% across retrospective COVID-19 and Influenza datasets and by an average of ~16% across six LLM backbones, according to the arXiv:2610.02744v1 paper. The world model achieved the best out-of-distribution Peak-MAE among all forecasting baselines, and the framework outperformed reinforcement-learning and optimal-control policy baselines. The system distills simulated future outcomes into reusable lessons while keeping protocol constraints fixed, allowing policy decisions to improve without sacrificing interpretability or controllability.", "body_md": "arXiv:2610.02744v1 Announce Type: new \nAbstract: Epidemic intervention policies are textual artefacts that human decision-makers interpret, justify, and revise through natural language, making large language models a natural candidate for epidemic policy reasoning. A naive LLM, however, lacks the epidemic dynamics needed to project intervention consequences, the quantitative surveillance signals required to assess severity, and the institutional constraints that define admissible actions. We present EpiWorld, 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 adaptive lessons accumulated through after-action analysis. Given a candidate intervention, the world model predicts regional epidemic evolution and enables fast counterfactual rollouts that provide feedback for policy selection and refinement. Outcomes of simulated futures are distilled into reusable lessons while protocol constraints remain fixed, allowing the decision process to improve without sacrificing interpretability or controllability. We evaluate both the world model and the end-to-end framework on retrospective COVID-19 and Influenza datasets: the world model achieves the best out-of-distribution Peak-MAE among all forecasting baselines, and the closed-loop framework reduces cumulative hospitalisation by up to 59% across datasets and by an average of ~16% across six LLM backbones, outperforming reinforcement-learning and optimal-control policy baselines.", "url": "https://wpnews.pro/news/epiworld-grounding-llm-policy-agents-in-epidemiological-world-models", "canonical_source": "https://arxiv.org/abs/2610.02744", "published_at": "2026-10-05 04:00:00+00:00", "updated_at": "2026-10-05 04:15:16.391707+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-research"], "entities": ["EpiWorld", "arXiv", "COVID-19", "Influenza"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/epiworld-grounding-llm-policy-agents-in-epidemiological-world-models", "markdown": "https://wpnews.pro/news/epiworld-grounding-llm-policy-agents-in-epidemiological-world-models.md", "text": "https://wpnews.pro/news/epiworld-grounding-llm-policy-agents-in-epidemiological-world-models.txt", "jsonld": "https://wpnews.pro/news/epiworld-grounding-llm-policy-agents-in-epidemiological-world-models.jsonld"}}