pii-skills-ablation-study 是做什么的？
与论文《当参数化知识胜出：小语言模型中智能体技能与工具使用的可控消融研究》相关的智能体技能注入消融研究，用于小语言模型（SLM）中的PII检测。
针对论文《当参数化知识胜出：小语言模型中智能体技能与工具使用的可控消融研究》的相关研究，这是一个关于小语言模型中智能体技能注入用于PII（个人身份信息）检测的消融研究。
本技能是对论文《When Parametric Knowledge Wins: A Controlled Ablation of Agent Skills and Tool Use for PII Detection in Small Language Models》的配套实现，专注于研究在小语言模型（SLM）中注入智能体技能（如工具使用）对PII检测性能的影响。通过消融实验，该项目系统地去除了模型已有的参数化知识、工具调用能力等组件，以量化评估每种技能对最终检测准确率的真实贡献。该技能支持在学术研究环境中复现论文实验，帮助开发者理解何时依赖内部参数化知识更有效，何时外部工具调用不可或缺。它主要适用于Python开发环境，依赖transformers、torch等库，并提供脚本化实验流程。
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与论文《当参数化知识胜出：小语言模型中智能体技能与工具使用的可控消融研究》相关的智能体技能注入消融研究，用于小语言模型（SLM）中的PII检测。
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Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
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Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.