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NCP-ArchPreview is introduced, a latent-space language model that pushes autoregressive pretraining beyond standard next-token prediction (NTP) and learns through Next Concept Prediction (NCP) to predict discrete concepts that span multiple tokens, introducing an explicit and more challenging concept-level objective while preserving standard token-level autoregressive generation.
Language models avoid overgeneralizations not through specific preemptive evidence, but by treating competing structures as indirect positive cues, reshaping our understanding of language acquisition mechanisms.
Achieving superior performance with one-third the resources, Qwen3.8-Flash-Next redefines efficiency in large-scale language models.
Achieving a staggering 90.4% reduction in robot-motion error, Hydra-0 redefines how we model and control robotic actions across varied environments.
Reallocating optimization effort based on reward saturation can boost performance by up to 9.2% in complex reasoning tasks.
