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Current OmniLLMs stumble when processing real-world, long-form audio-visual content, achieving only ~35-65% accuracy on a new benchmark designed to test long-term memory and fine-grained understanding.
Achieve globally consistent 3D reconstruction over sequences exceeding 19,000 frames by combining test-time training with sliding window attention, outperforming prior state-of-the-art methods by over 74% on ATE on KITTI.
