那么，OpenClaw的价值究竟是什么？真的无所不能吗？它为AI产业化落地带来了什么？真的能推动我们走向通用AGI吗？以及，在如今中国企业Agent落地正进入系统繁多却彼此割裂的深水区，OpenClaw这一路径，是否能够改变现状？
2025年，大连市重大项目压茬推进，恒力重工二期、中粮油脂等项目建成投产，大连金州湾国际机场、大连长海跨海大桥、恒力重工合作创新暨海工科技产业园等项目加快建设，辽东半岛水资源配置工程可研报告获国家批复、迈出关键性实质步伐。“抓实项目才能抓实工作”实践效应凸显。
Получивший взятку в размере 180 миллионов экс-мэр российского города обратился к суду14:53，推荐阅读爱思助手下载最新版本获取更多信息
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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.，更多细节参见哔哩哔哩
Фото: Kremlin Pool / Global Look Press