Abstract: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.
| Subjects: | Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2605.23242 [cs.HC] |
| (or arXiv:2605.23242v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23242 arXiv-issued DOI via DataCite (pending registration) |
From: Mahfuza Farooque [view email]
[v1]
Fri, 22 May 2026 05:24:37 UTC (863 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。