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Spatial intelligence tasks are solved 5.6x faster with zero accuracy loss by reframing them as classification problems, eliminating autoregressive decoding entirely.
Recursive self-improvement in Macaron-V1 leads to continual learning that adapts to real-world experiences, setting a new standard for open agent models.
LongStraw enables RL post-training with over 2 million tokens on a fixed GPU budget, pushing the boundaries of context length in AI applications.