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Still, for all the clever innovations that have made cabins safer, the economics of flight have also potentially made them more dangerous. Planes once furnished with plush recliners and wide-open aisles are now packed with more passengers each year. Those in first class lounge in lie-flat seats with built-in air bags in their seat belts, while those in economy sit shoulder to shoulder and knee to seat back, hoping they won’t crack heads in a storm. In 2021, the F.A.A. published a study on emergency evacuations which concluded that current cabin-seating requirements “can accommodate and not impede egress for 99% of the American population.” But last year a review panel from the National Academies of Sciences, Engineering, and Medicine found that the study was flawed. The F.A.A. trials had included only volunteer passengers with no physical limitations, and none of them was over sixty years old.，这一点在币安_币安注册_币安下载中也有详细论述
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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.