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Expanding tokenizers in-place can lead to up to 4x fewer tokens for new languages, drastically improving decoding efficiency for multilingual models.
A fully onboard multi-agent robotic system can outperform cloud-dependent models in efficiency and task execution while maintaining strong generalization capabilities.