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A low-rank tensor decomposition of a stochastic basis coupled with a learnable spatial dictionary unlocks accurate and efficient uncertainty quantification for random PDEs, outperforming existing methods by a significant margin.
Clip-level captions compress away the continuous visual dynamics needed for true long-horizon reasoning; Kairos restores this signal with dense, time-resolved annotations tracking actions, entities, and attributes across 10-to-30-minute videos.