直到今年6月的伊以冲突发生后，被斥为“毒蛇”的凯瑟琳依然没有被伊朗人忘记。伊朗前议员穆斯塔法·卡瓦克比扬认为，以军能过于精准地定位并摧毁伊朗的关键目标（包括那些高度保密的目标），这个打着记者招牌的女间谍“功不可没”。
“When I was a teenager, cricket in Zimbabwe was almost exclusively played and supported by white people,” he says. “Besides the accents and topics of conversation, you could tell this by the way they would applaud and chant. It had a particular energy. The most animated fans were usually the ones who had too much beer and hurled abuse at the players on the boundary.”
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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.
（三）签发不记名提单或者签发指示提单经空白背书的，凭提单向提单持有人交付；
Since the degree of each l_i(x) is , then the degree of