2024年12月25日 星期三 新京报
她於2021年因替愛潑斯坦招募並販運四名未成年少女以供性剝削而被定罪，當時他是她的男友。愛潑斯坦在2019年於監獄中自殺，當時正等待性交易指控的審判。他先前在2008年因引誘未成年人賣淫而被定罪，並因一項被廣泛視為寬鬆的協議而入獄一年，當時他已被指控販運數十名女性與女孩。
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An Interesting Find: STM32 RDP1 Decryptor2026.03.01 :: Karolis Stasaitis :: #stm32 #reverse engineering #rdp
In the next room, another editor put together a scene featuring AI-generated video of jet fighters preparing to take off. This helps Vigloo cut production costs down to 10% or less of traditional filmmaking, Choi said.
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.