arXiv AI & Jobs Research (Sha Hu) • Published: 2026-02-26
An artificial intelligence (AI) model can be viewed as a function that maps inputs to outputs in high-dimensional spaces. Once designed and well trained, the AI model is applied for inference. However, even optimized AI models can produce inference errors due to aleatoric and epistemic uncertainties. Interestingly, we observed that when inferring m
arXiv AI & Jobs Research (Aaron Reich, Diana Wolfe, Matt Price, Alice Choe, ) • Published: 2026-02-26
Artificial intelligence tools are increasingly embedded in everyday work, yet employees' uptake varies widely even within the same organization. Drawing on sociotechnical and work design perspectives, this research examines whether motivational job characteristics and multidimensional AI threat perceptions jointly predict workplace AI adoption and
arXiv AI & Jobs Research (Radha Sarma) • Published: 2026-02-26
AI systems are increasingly deployed in high-stakes contexts -- medical diagnosis, legal research, financial analysis -- under the assumption they can be governed by norms. This paper demonstrates that assumption is formally invalid for optimization-based systems, specifically Large Language Models trained via Reinforcement Learning from Human Feed
arXiv AI & Jobs Research (Wenjia Wang, Liang Pan, Huaijin Pi, Yuke Lou, Xuqi) • Published: 2026-02-26
Human behaviors in the real world naturally encode rich, long-term contextual information that can be leveraged to train embodied agents for perception, understanding, and acting. However, existing capture systems typically rely on costly studio setups and wearable devices, limiting the large-scale collection of scene-conditioned human motion data
arXiv AI & Jobs Research (Jeremy P Parker, Tobias M Schneider) • Published: 2026-02-26
Generative artificial intelligence has shown remarkable success in synthesizing data that mimic complex real-world systems, but its potential role in the discovery of mathematically meaningful structures in physical models remains underexplored. In this work, we demonstrate how a generative diffusion model can be used to uncover previously unknown
arXiv AI & Jobs Research (Yonglin Chen, Jingjing Zhang, Kezhuo Wang, Pengche) • Published: 2026-02-26
Resilience is a key factor affecting children's mental wellbeing and future development. Yet, limited HCI research has explored how to help children build resilience through adversarial experiences. Informed by a formative study with elementary school teachers and professional psychologists, we design TaleBot, an AI-empowered system that supports c
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