发展要高质量，立法也要高质量。正在召开的十四届全国人大四次会议上，生态环境法典草案、民族团结进步促进法草案、国家发展规划法草案提请审议。
The sight of a delectable plate of lasagna or the aroma of a holiday ham are sure to get hungry bellies rumbling in anticipation of a feast to come. But although we’ve all experienced the sensation of “eating” with our eyes and noses before food meets mouth, much less is known about the information superhighway, known as the vagus nerve, that sends signals in the opposite direction — from your gut straight to your brain.
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REST API reference + curl examples: REST-API.md
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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.，更多细节参见官网