2026-02-28 00:00:00:03014268710http://paper.people.com.cn/rmrb/pc/content/202602/28/content_30142687.htmlhttp://paper.people.com.cn/rmrb/pad/content/202602/28/content_30142687.html11921 本版责编：纪雅林 管璇悦 翟钦奇
巴罗表示，军事行动的无限期延长若缺乏明确目标，便有可能引发恶性循环，从而将伊朗乃至整个地区拖入长期动荡的境地。（央视新闻）
。业内人士推荐咪咕体育直播在线免费看作为进阶阅读
/ downstream-lxml (push) Successful in 1m41s，这一点在币安_币安注册_币安下载中也有详细论述
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.
For a small NSFW audio platform run by a solo developer, “true” blackbox DRMs running with TEEs are not a realistic option. Which brings me to the point I actually want to make: