We present Cogniscope, an open evaluation framework for studying longitudinal early-risk AI systems under controlled behavioral drift, sparse observations, delayed evidence, and heterogeneous progression patterns. Cogniscope combines two complementary components: a synthetic simulation engine that generates privacy-preserving longitudinal behavioral traces aligned with configurable latent risk trajectories, and a browser-based data-collection instrument implemented as a Chrome extension for capturing naturalistic video interaction telemetry and micro-question responses during YouTube playback. The released benchmark includes 200,000 simulated video-interaction records from 200 users over 200 days, a 504-session schema-aligned synthetic deployment dataset across nine behavioral profiles, an 18-table relational schema, baseline evaluation scripts, and time-aware metrics including Early Risk Detection Error (ERDE) and time-to-detection (TTD). We emphasize that Cogniscope is not a diagnostic system and does not claim clinical validity. Instead, it provides a reusable testbed for evaluating how sequential models behave under known longitudinal challenges before deployment with real human-subject data. Experiments show that simple behavioral coherence signals separate simulated risk states under controlled priors, while rule-based deployment-profile classification remains challenging, motivating learned temporal models and robust evaluation protocols.

翻译：我们提出Cogniscope——一个开放的评估框架，用于研究受控行为漂移、稀疏观测、延迟证据及异质性进展模式下的纵向早期风险人工智能系统。Cogniscope包含两个互补组件：一个合成模拟引擎，可生成符合可配置潜在风险轨迹、保护隐私的纵向行为痕迹；以及一个基于浏览器的数据采集工具（以Chrome扩展形式实现），用于在YouTube播放过程中捕获自然交互视频遥测数据与微问答响应。已发布的基准数据集包含20万条来自200名用户、持续200天的模拟视频交互记录；涵盖9种行为谱型、含504个会话的符合模式化架构的合成部署数据集；18表关系模式；基线评估脚本；以及包括早期风险检测误差（ERDE）和检测时间（TTD）在内的时效性指标。我们强调，Cogniscope并非诊断系统，亦不宣称具备临床有效性。相反，它提供了一个可复用的测试平台，用于评估序列模型在真实人类受试者数据部署前，面对已知纵向挑战时的表现。实验表明，在受控先验条件下，简单的行为一致性信号可区分模拟风险状态，而基于规则部署的谱型分类仍具挑战性，这激励了学习型时序模型与稳健评估协议的研究。