EpiWorld: Grounding LLM Policy Agents in Epidemiological World Models
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 throug...
| Authors | Zeeshan Memon, Yiqi Su, Kai Shu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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