获奖小说《轨道》同样以“太空旅行促进全球合作与生命敬畏”为主题，描绘了多国宇航员在空间站的故事。但若说曾经可能忽视太空探索的阴暗面，当今时代绝无可能。1960年代，美苏太空计划是两大阵营军事实力的投射；2020年代，科技巨头贝索斯与马斯克成为美国航天业复兴的关键推手，而中美间的星际地缘政治博弈正在成形。美国宇航局更计划在2030年前将核反应堆送上月球。
特朗普用"后果很严重"向伊朗发出威胁 20:59
，更多细节参见易歪歪
无论是豪宅还是跑车，巴菲特始终避免进行与财富匹配的奢侈消费。2014年在伯克希尔股东大会上他坦言："超过某个临界点后，生活水平与生活成本并不成正比。拥有六七处房产或大量奢侈品不会让我更快乐，反而可能更糟。"。比特浏览器对此有专业解读
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Abstract:Humans shift between different personas depending on social context. Large Language Models (LLMs) demonstrate a similar flexibility in adopting different personas and behaviors. Existing approaches, however, typically adapt such behavior through external knowledge such as prompting, retrieval-augmented generation (RAG), or fine-tuning. We ask: do LLMs really need external context or parameters to adapt to different behaviors, or do they already have such knowledge embedded in their parameters? In this work, we show that LLMs already contain persona-specialized subnetworks in their parameter space. Using small calibration datasets, we identify distinct activation signatures associated with different personas. Guided by these statistics, we develop a masking strategy that isolates lightweight persona subnetworks. Building on the findings, we further discuss: how can we discover opposing subnetwork from the model that lead to binary-opposing personas, such as introvert-extrovert? To further enhance separation in binary opposition scenarios, we introduce a contrastive pruning strategy that identifies parameters responsible for the statistical divergence between opposing personas. Our method is entirely training-free and relies solely on the language model's existing parameter space. Across diverse evaluation settings, the resulting subnetworks exhibit significantly stronger persona alignment than baselines that require external knowledge while being more efficient. Our findings suggest that diverse human-like behaviors are not merely induced in LLMs, but are already embedded in their parameter space, pointing toward a new perspective on controllable and interpretable personalization in large language models.