Tootfinder

SCENIC: A Location-based System to Foster Cognitive Development in Children During Car Rides
Liuqing Chen, Yaxuan Song, Ke Lyu, Shuhong Xiao, Yilang Shen, Lingyun Sun
https://arxiv.org/abs/2508.17058

SCENIC: A Location-based System to Foster Cognitive Development in Children During Car Rides
Car-riding is common for children in modern life. Given the repetitive nature of daily commutes, they often feel bored and turn to electronic devices for entertainment. Meanwhile, the rich and dynamic scenery outside the car naturally attracts children's curiosity and offers valuable resources for cognitive development. Our formative study reveals that parents' support during car rides is often fleeting, as accompanying adults may struggle to consistently guide children's exploration. To addres…

Trainee Action Recognition through Interaction Analysis in CCATT Mixed-Reality Training
Divya Mereddy, Marcos Quinones-Grueiro, Ashwin T S, Eduardo Davalos, Gautam Biswas, Kent Etherton, Tyler Davis, Katelyn Kay, Jill Lear, Benjamin Goldberg
https://arxiv.org/abs/2509.17888

Trainee Action Recognition through Interaction Analysis in CCATT Mixed-Reality Training
This study examines how Critical Care Air Transport Team (CCATT) members are trained using mixed-reality simulations that replicate the high-pressure conditions of aeromedical evacuation. Each team - a physician, nurse, and respiratory therapist - must stabilize severely injured soldiers by managing ventilators, IV pumps, and suction devices during flight. Proficient performance requires clinical expertise and cognitive skills, such as situational awareness, rapid decision-making, effective com…

Secure Multi-LLM Agentic AI and Agentification for Edge General Intelligence by Zero-Trust: A Survey
Yinqiu Liu, Ruichen Zhang, Haoxiang Luo, Yijing Lin, Geng Sun, Dusit Niyato, Hongyang Du, Zehui Xiong, Yonggang Wen, Abbas Jamalipour, Dong In Kim, Ping Zhang
https://arxiv.org/abs/2508.19870

Secure Multi-LLM Agentic AI and Agentification for Edge General Intelligence by Zero-Trust: A Survey
Agentification serves as a critical enabler of Edge General Intelligence (EGI), transforming massive edge devices into cognitive agents through integrating Large Language Models (LLMs) and perception, reasoning, and acting modules. These agents collaborate across heterogeneous edge infrastructures, forming multi-LLM agentic AI systems that leverage collective intelligence and specialized capabilities to tackle complex, multi-step tasks. However, the collaborative nature of multi-LLM systems int…