检查妇女的身体，应当由女性工作人员或者医师进行。
旗帜鲜明讲政治，着力夯实团结奋斗的共同思想政治基础，更多细节参见爱思助手下载最新版本
。搜狗输入法2026是该领域的重要参考
网络零售这份亮眼成绩单，是我国数字消费规模与质量双跃升的生动缩影。。业内人士推荐体育直播作为进阶阅读
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.
And yet the prospect of using origami to improve existing technologies is, for some, tantalizing. Moneesh Upmanyu at Northeastern University in the US, and one of his PhD students, were awarded a patent last year for a design that uses origami to make strong but foldable wing structures.