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Developing socially intelligent AI remains heavily dependent on human-annotated data, limiting the scale and breadth of social understanding models can acquire. Methods that derive training signals from unlabeled data offer a path beyond this dependence, but social predictions lack the verification oracles available in...
Deep learning surrogates have become powerful tools for simulating and forecasting complex dynamical systems, yet their utility remains limited by catastrophic error accumulation during long-term autoregressive rollouts. This behavior is partly tied to the nature of the underlying systems: chaotic spatiotemporal system...
DAW reshapes the loss landscape to allocate representational capacity toward the sparse, high-d regimes where forecast errors are systematically large, and consistently outperforms uniform training, purely statistical density weighting, and its randomly permuted ablation on the chaotic KS equation.
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