6. 消息送入 Agent，AI 开始执行
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.
# Define data-model for an input-string with a cursor，详情可参考同城约会
精打细算的财务CEO或许能守住丰田的现在。。关于这个话题，下载安装汽水音乐提供了深入分析
which allows us to progress the bar now and still have one step left。heLLoword翻译官方下载是该领域的重要参考
Several updates relate to how users communicate with Google devices, with context improvements promised for asking Gemini general questions and giving everyday commands. A common complaint with Google Home users, getting cut-off while speaking, has also been addressed, with Kattukaran saying that the team has "significantly reduced these premature cut-offs to ensure Gemini correctly understands you, enabling smoother and more fluid turn-taking during live conversations."