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Dataset representation in NLP is alarmingly uneven, with geographic blind spots that could skew AI development and policy.
Today's best AI agents still fail more than half the time on real-world tasks combining vision, search, and coding, revealing critical gaps in reasoning and tool use.
Stabilizing test-time training with an elastic prior lets you reconstruct 4D scenes from long video sequences without catastrophic forgetting, even with smaller memory chunks.
A 1000x larger video reasoning dataset reveals early signs of emergent generalization, offering a new foundation for training and evaluating spatiotemporal AI.
