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在2026年的就业市场中，熟练掌握AI工具进行协同办公已不再是加分项，而是类似“会用Office”的基础职业准则 [4, 25]。普通人的核心竞争力正发生显著位移：从过去的“执行力”转向“策划力（Curation）”与“裁判权（Judgment）” [4]。。同城约会对此有专业解读
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2月26日，日本大阪大学研究团队在《科学》（Science）在线发表研究论文Reconstitution of sex determination and the testicular niche using mouse pluripotent stem cells，报告他们利用小鼠多能干细胞对睾丸体细胞进行的重建工作。该重建过程重现了性别决定过程，产生了形成生精小管和相邻间质组织的细胞类型，重建的睾丸组织整合了多能干细胞来源的原始生殖细胞，并支持其分化为精原干细胞。这些精原干细胞在移植到睾丸后分化为具有功能的精子，且这些精子成功使卵子受精，并发育成健康且有生育能力的后代。
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.