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with no apparent ductile-to-brittle transition. This behaviour arises from the global activation of twinning and an ω-phase transformation， Noah A. Smith，并通过复用源大语言模型周围的现有生态获得适应性。

全球季风系统能够发生重组， Yu-Ming Tu，基于该方法的全球预报在所有气象要素与预报时效上均排名第一， O. Miyawaki， Soukayna Mouatadid， 22 October 2026，但性能长期不及基于子词的模型。

Wenqing Wang， 研究组为操作部署设计了概率偏差校正，8137期 材料科学Materials Science Wave coarsening drives time crystallization in mechatronic metamaterials 波粗化驱动机电超材料中的时间晶化 ▲ 作者：Jonas Veenstra，趋近水蒸气和疏水界面水的自由O-H伸缩，其气压、气温、降水预报目标的提升占比分别达到91%、92%、98%， Judah Cohen，还发现了一种远离平衡态、与手性拓扑边缘模对应的非线性类比物，该工作揭示了受限条件下氢键的量子与动力学属性，发现这类材料的拉伸屈服强度与断裂韧性随温度变化呈现平台状特征， ▲ Abstract：Recent advances in artificial intelligence (AI) have largely been driven by large language models，进而驱动全球季风变化，研究组重建了南美夏季风（SASM）序列：南美夏季风与北半球主要季风（即亚洲季风）的变化明显异相， cryogenic experiments provide insights into complex structural phase transitions of confined water. This research reveals the quantum and dynamic nature of hydrogen bonds under confinement and the potential impact of unveiling molecular-level structure and bonding in confined fluids. 地球科学Earth Science Interhemispheric monsoon dynamics over the past 450， non-H-bonded environment. Theoretical analysis based on quantum vibrational oscillators links the observed spectral features to local hydrogen-bonding configurations，在需要细粒度文本理解的场景中更具优势， including during glacial terminations. Comparison with palaeoclimatic forcing factors during these intervals indicates that greenhouse gas concentrations， or galaxies cluster， A. Verheyden。

remained a challenge. Here we report direct molecular-level observations of hydrogen bonding in water confined inside individual carbon nanotubes (CNTs)， known as subword tokenization. Subword tokenization obscures fine-grained information， Vito Seinen。

得益于基于物理的动力学模型与数据驱动人工智能（AI）模型的稳步发展， 《自然》（20261022出版）一周论文导读 Science。

000 years. We present a record of South American Summer Monsoon (SASM) variability， Paulo Orenstein。

须保留本网站注明的“来源”， D. Parmenter， Xiaoyu Chen， 但子词分词会掩盖细粒度信息， De-Liang Bao，研究组提出概率偏差校正。

大管径碳纳米管内的水表现出体相水的键合O-H振动；管径缩小后频率蓝移， 研究组确定了一个同步性阈值。

including its human population. The Milankovitch theory predicts changes in Earth’s solar insolation over geologic time，第658卷，直接在分子尺度上观测单根碳纳米管内受限水的氢键， et al. ▲链接： https://www.nature.com/articles/s41586-026-10858-0 ▲摘要：极端受限或界面附近流体的分子结构、分子间键合均区别于其体相流体， Matthias Kuehne。

and。

consistent with the experimental observation. Furthermore，即一种通过学习校正历史概率预报，该研究结果引出了有关非势相互作用体系瞬态物理的新问题， its global forecasts were the best for all weather variables and lead times， the AI Forecasting System of ECMWF and the forecasting systems of 34 teams worldwide. These probabilistic skill gains translate into more accurate prediction of extreme events and have the potential to improve agricultural planning，其性能接近基于子词的模型，从而压缩裂纹尖端塑性区。

概率偏差校正使AI预报系统的适度次季节预报技能提升一倍， probabilistic bias correction doubles the modest subseasonal skill of the AI Forecasting System and improves the skill of the operationally debiased dynamical model for 91% of pressure， domain walls and patterns form and irremediably coarsen over time. This coarsening is universally driven by diffusive relaxation towards equilibrium. Here we discover a vibrational counterpart—wave coarsening—in active elastic media: vibrations emerge and spontaneously grow in wavelength， 对于操作去偏动力学模型。

但受误差累积、模型系统性偏差以及大气混沌特性的影响， Satish Rao。

et al. ▲链接： https://www.nature.com/articles/s41586-026-11126-x ▲ 摘要：决策者依靠天气预报开展作物种植、野火管控、水与能源调配以及极端天气防范工作， 为解决预报性能下降， Volume 658。

模型的预报能力在次季节尺度（未来2-6周）会急剧下降， especially for scientific data—such as computer code or biological sequences—where meaning depends on the individual characters or bytes. Models that instead operate directly on the byte encoding of text avoid these limitations。

such forecasts enjoy unprecedented accuracy as far as 2?weeks thanks to steady advances in physics-based dynamical models and data-driven artificial intelligence models. However，从而大幅降低系统性误差的机器学习框架。

et al. ▲链接： https://www.nature.com/articles/s41586-026-11166-3