This research proposes CLEO-2, a framework to predict optimal handoff timing from interaction history, integrating temporal interaction modeling with learned attention mechanisms to forecast ideal control transfer moments.
Key findings
CLEO-2 predicts handoff timing by processing dialogue context, user engagement signals, and task progression metrics.
Addresses limitations in existing handoff prediction methods by modeling temporal dynamics, incorporating uncertainty quantification, and enabling personalized predictions.
Includes a comprehensive validation plan with benchmark evaluation, ablation studies, and human-in-the-loop experiments.
Limitations & open questions
The framework's effectiveness is yet to be fully validated through human-in-the-loop experiments.
The model's ability to generalize across different domains and tasks remains to be tested.