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Traditional causal inference requires hand-crafted pipelines and bespoke estimator training for every dataset, but causal foundation models can now infer treatment effects entirely zero-shot via in-context learning.
Global consistency in Text-to-SVG generation can be achieved without additional training, thanks to a novel adaptive sampling strategy that intelligently navigates stroke decisions.
TUE-Detector outperforms traditional methods by using a multi-layered language model to invoke specialized tools for uncovering subtle artifacts in AI-generated videos.
