EpiEvolve achieves 0.629 accuracy in streaming COVID-19 forecasting by using episodic memory, reflection on delayed labels, and regime-aware retrieval, outperforming static LLMs (0.561) and CDC ensembles (0.325) while halving recovery lag after regime shifts.
arXiv preprint arXiv:2602.00299 , year=
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MechSim is a mechanism-grounded framework that represents simulators via structured schemas and uses constrained LLM agents to generate evidence-based explanations linking outcomes to underlying mechanisms.
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EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts
EpiEvolve achieves 0.629 accuracy in streaming COVID-19 forecasting by using episodic memory, reflection on delayed labels, and regime-aware retrieval, outperforming static LLMs (0.561) and CDC ensembles (0.325) while halving recovery lag after regime shifts.
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Simulate, Reason, Decide: Scientific Reasoning with LLMs for Simulation-Driven Decision Making
MechSim is a mechanism-grounded framework that represents simulators via structured schemas and uses constrained LLM agents to generate evidence-based explanations linking outcomes to underlying mechanisms.