Automated Alzheimer's Disease (AD) screening has predominantly followed the inductive paradigm of pattern recognition, which directly maps the input signal to the outcome label. This paradigm sacrifices construct validity of clinical protocol for statistical shortcuts. This paper proposes Agentic Cognitive Profiling (ACP), an agentic framework that realigns automated screening with clinical protocol logic across multiple cognitive domains. Rather than learning opaque mappings from transcripts to labels, the framework decomposes standardized assessments into atomic cognitive tasks and orchestrates specialized LLM agents to extract verifiable scoring primitives. Central to our design is decoupling semantic understanding from measurement by delegating all quantification to deterministic function calling, thereby mitigating hallucination and restoring construct validity. Unlike popular datasets that typically comprise around a hundred participants under a single task, we evaluate on a clinically-annotated corpus of 402 participants across eight structured cognitive tasks spanning multiple cognitive domains. The framework achieves 90.5% score match rate in task examination and 85.3% accuracy in AD prediction, surpassing popular baselines while generating interpretable cognitive profiles grounded in behavioral evidence. This work demonstrates that construct validity and predictive performance need not be traded off, charting a path toward AD screening systems that explain rather than merely predict.

翻译：自动化阿尔茨海默病（AD）筛查主要遵循模式识别的归纳范式，直接将输入信号映射至结果标签。该范式为追求统计捷径而牺牲了临床方案的结构效度。本文提出智能认知剖析（ACP），这是一个智能体框架，旨在将自动化筛查与跨多个认知领域的临床方案逻辑重新对齐。该框架并非学习从转录文本到标签的隐晦映射，而是将标准化评估分解为原子级认知任务，并协调专门的LLM智能体以提取可验证的评分基元。我们设计的核心在于通过将所有量化工作委托给确定性函数调用，实现语义理解与测量过程的解耦，从而缓解幻觉问题并恢复结构效度。与通常仅包含单一任务下约百名参与者的常用数据集不同，我们在一个包含402名参与者、涵盖八个结构化认知任务（横跨多个认知领域）的临床标注语料库上进行评估。该框架在任务检查中实现了90.5%的分数匹配率，在AD预测中达到85.3%的准确率，超越了常用基线方法，同时生成基于行为证据的可解释认知剖面。本研究表明，结构效度与预测性能无需相互妥协，为构建能够解释而不仅仅是预测的AD筛查系统开辟了道路。