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Reward modeling inference costs plummet by up to 7x by performing judgments entirely in a compressed latent space, bypassing token-by-token generation.
Atomic visual perception in MLLMs is largely unsolved, with no model surpassing 60% accuracy on a new benchmark designed to isolate perceptual capabilities.
Kimi K3's innovative architecture achieves a 2.5x scaling efficiency improvement, enabling robust performance across diverse long-horizon tasks.
ILLUME-X achieves unprecedented quality in free-form interleaved text-image generation, setting a new benchmark for multimodal models.