南方周末：在“权责利”三位一体的制度闭环下，协会的组织架构应如何设计？
Finally, there is the synthetic-data-driven, product closed-loop flywheel. Noin centers its approach on proprietary synthetic data, building a training system tailored to embodied manipulation: through scalable task generation, action/trajectory generation, and filtering mechanisms, it continuously produces high-quality training data that covers long-tail scenarios, which is then used to train embodied foundation models with stronger generalization. Compared with routes that rely heavily on demonstrations and real-world data collection, the company places greater emphasis on a “controllable, scalable, and iterative” synthetic-data pipeline, and feeds back product and real-hardware runtime signals—such as feedback, failure cases, and abstractions of critical scenarios—into its data generation and evaluation system, forming a closed-loop flywheel of “product feedback → synthetic enhancement → training iteration → experience improvement.” Backed by a high-quality synthetic-data pipeline, it continues to drive model capability gains, creating a hard-to-replicate self-evolving system and cementing long-term technical barriers. This route has a high engineering threshold; Noin has already validated the key links and established a sustainable gain-and-verification system for embodied manipulation and task generalization.
。电影对此有专业解读
Runtime evaluation support
36氪获悉，纳芯微在港交所公告，公司2025年营业总收入33.68亿元，同比增长71.8%；归属于公司所有者的净亏损2.41亿元，上年同期亏损4.03亿元，同比减亏。公司已申请公司H股于2026年3月3日上午九时正于香港联交所恢复买卖。
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