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论文提出 CogDrills-4D（CogDrift-4D），一种层级式 4D Transformer 多模态框架，通过 18 个月内七次非脚本化交互的行为数据建模时间性行为衰退，用于轻度认知障碍（MCI）早期检测，分类准确率达 0.733±0.13。
The study compares longitudinal, cross-sectional, and graph-based models for predicting adolescent substance use onset using ABCD Study data, showing that combined approaches achieve better accuracy with AUC-ROC values above 0.79.
DMT-CBT proposes a framework for longitudinal therapeutic state modeling in CBT counseling, addressing the need for multi-session, multimodal inference and intervention. It introduces DMTCorpus, a synthetic multi-session dataset, and shows improvements in counseling fidelity and therapeutic alliance over existing methods.
LiFT is a longitudinal instruction fine-tuning framework that unifies diverse temporal NLP tasks under a shared instruction schema with curriculum-based training. Evaluated across OLMo, LLaMA, and Qwen models, LiFT consistently outperforms base-model in-context learning, especially on out-of-distribution data and rare change events.