APL-1702的获批精准破解了上述痛点，其创新光动力药械联用方案构建了差异化核心竞争力。产品采用“专利脂溶性光敏剂+突破性超低功率LED光源+首创便携式设计”的组合式创新，通过光敏剂在病变细胞内特异性蓄积，经光照后产生活性氧诱导病变细胞凋亡，实现对病变组织的精准靶向治疗。国际多中心Ⅲ期临床试验数据进一步验证了其疗效可靠性，为商业化落地提供强支撑：CIN2治疗组57.5%的患者在首次治疗后6个月的组织病理学结果显示已转为正常组织或LSIL，而安慰剂组的患者为30.6%（p=0.0009）。
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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.
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