Lenovo announced a funky foldable version of its flagship Legion Go 2 gaming handheld today at Mobile World Congress 2026. (The news leaked out last week.) Mashable also got the chance to go hands-on with the concept handheld at a demo event ahead of MWC, and it's a cool little gadget.
‘부화방탕 대명사’ 북한 2인자 최룡해의 퇴장 [주성하의 ‘北토크’]，更多细节参见旺商聊官方下载
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She has previously said that her husband flew with Epstein for charitable trips but that she did not recall ever meeting Epstein. She had also interacted with Maxwell, Epstein’s former girlfriend and confidant, at conferences hosted by the Clinton Foundation.
按照問卷說法，「樓換樓」是指以原來的宏福苑單位，換取與業權收購「價格相若」的全新居屋單位，並非「面積相若」。。咪咕体育直播在线免费看对此有专业解读
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.