Kailai Cui

Kailai Cui

Resource-efficient multimodal AI systems through task-aware inference and serving

Agentic Edge-Cloud Orchestration for Resource-Efficient Daily Food Inventory Monitoring with Egocentric Video

Published in Manuscript under review, 2026

Recommended citation: Kailai Cui, Kaylee Yaxuan Li, Chaoyu Zhang, Peizhou Huang, Hao Chen, Jianzhong Zhang, Ke Sun, Kang G. Shin, "Agentic Edge-Cloud Orchestration for Resource-Efficient Daily Food Inventory Monitoring with Egocentric Video." Manuscript under review.

InvAgent treats daily food-inventory monitoring as sparse-evidence inference: brief observations reveal item use and remaining amount, while whole-video VLM analysis spends computation on irrelevant frames. An edge context builder summarizes item interactions, and a bounded agentic workflow selects amount-evidence windows and revisits unresolved items. Across approximately 39 hours from seven households, the system reduced VLM input tokens by 9.9x versus 1-FPS whole-video inference while increasing amount-aware inventory F1 from 66.6% to 68.9%.