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ArcAD reshapes cold-start anomaly detection by synthesizing pseudo-anomalies and clustering limited normal samples, leading to unprecedented performance gains.
Achieve nuanced control over portrait animations鈥攅ven for subtle states like thinking or drowsiness鈥攂y using hierarchical agent planning to translate high-level labels into precise eye movements.
GaLa's hypergraph representation reveals hidden semantic relationships in multimodal data, leading to a dramatic boost in procedural planning accuracy.
By disentangling structure and motion in the latent space, CoWVLA achieves superior visuomotor learning compared to standard world-model and latent-action approaches.
