Guar gum, a plant colloid, costs less than agar and is better suited for growing thermophilic bacteria, but is also more difficult to handle, being more viscous and less transparent. The bacterial polysaccharide xanthan is cheaper as well but forms weaker jellies that, as with carrageenan, might result in puncturing its surface. Other colloids, like alginate (from brown seaweed) and gellan gum (from a bacterium), don’t set solely based on temperature and require additives for gelation. These additives might interfere with microbial growth and make the preparation of those jellies less handy than agar plates.
2025年11月，广东省梅州市梅县区雁洋镇南福村，黄澄澄的柚子挂满枝头，柚香淡淡萦绕。
。快连下载安装是该领域的重要参考
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.
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