19:34, 27 февраля 2026Интернет и СМИ
We haven't had a new film from Gore Verbinski for nine years. But the director who brought us the first three Pirates of the Caribbean movies, the nightmare-inducing horror of The Ring (2002), and the Oscar-winning hijinks of Rango (2011) is back in peak form with Good Luck, Have Fun, Don't Die. It's a darkly satirical, inventive, and hugely entertaining time-loop adventure that also serves as a cautionary tale about our widespread online technology addiction.
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观点本无对错，但 AI 时代的机会，本质上是属于少数人的机会，甚至比移动互联网时代的红利窗口更窄。，更多细节参见快连下载安装
第三十四条 组织、领导传销活动的，处十日以上十五日以下拘留；情节较轻的，处五日以上十日以下拘留。
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.