arXiv Computation and Language
EpiWorld: Grounding LLM Policy Agents in Epidemiological World Models
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
arXiv AI
Jun 3
SL-BiLEM: Structured Learnable Behavior-in-the-Loop Epidemic Modeling for Forecasting and Policy Evaluation
arXiv:2605. 26704v2 Announce Type: replace-cross Abstract: Epidemic forecasting faces a fundamental challenge: human behavior dynamically responds to disease spread, creating feedback loops that induce distribution shifts at policy intervention points.
arXiv AI
Jun 3
The Epi-LLM Framework: probing LLM behavioral priors through epidemiological agent-based models
arXiv:2606. 02867v1 Announce Type: cross Abstract: Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging.
arXiv AI
2d ago
Graph World Models for Constrained Epidemic Policy Planning
Graph World Models for Constrained Epidemic Policy Planning introduces EpiMind, a graph-factored recurrent state-space model that generates joint policy-conditioned rollouts from regional latent beliefs. The framework uses graph-temporal ADMM to optimize regional interventions, enforce shared-resource feasibility, and evaluate temporal specifications under the learned model. EpiMind achieves a 29% reduction in admission RMSE compared to graph-free dynamics, plans within 1–5% of the best feasible constant policy while guaranteeing shared-budget feasibility, and outperforms all deployable baselines across three resource budgets in real-context evaluation.
arXiv AI
Jun 4
Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models
arXiv:2606. 04562v1 Announce Type: new Abstract: Purpose The WHO's COVID-19 non-pharmaceutical interventions (e.
Hugging Face Trending Papers
Jun 3
Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models
Purpose The WHO's COVID-19 non-pharmaceutical interventions (e. g.
arXiv Machine Learning
Jun 5
Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions
arXiv:2606. 05692v1 Announce Type: new Abstract: Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes.