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LLM agent simulations are no longer black boxes: CAMO reveals the hidden causal pathways from individual agent actions to emergent social behaviors.
Neural operators can now achieve both high accuracy and speed for dynamical systems modeling, thanks to a MeanFlow enhancement that recovers lost small-scale details without the inference overhead of diffusion models.
Discovering causal relationships that remain stable across different online market environments allows for more effective and generalizable governance policies.
Uni-Flow achieves faster-than-real-time modeling of complex multiscale flows by decoupling temporal evolution and spatial refinement, opening doors for deployable surrogates in scientific machine learning.
