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cs.AI2026
MatrAIx: Simulating the World with 8.3 Billion Persona Agents
Xiaomin Li, Yuexing Hao, Jianheng Hou +90
Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and inter…
cs.CL2026
CurveShift: Is Agent Progress Scalar? Separating Level from Shape
Hanwen Xing, Pengyun Wang, BingXu Meng +8
Progress in large language models is often summarized using a single scalar measure, such as a time horizon, a latent ability estimate, or an aggregate benchmark score. These summa…
cs.LG2026
Breaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning
Xinyan Han, Yan Lu, Xiaoyu Lin +5
Tabular data synthesis aims to generate high-quality data while preserving privacy. However, we find that existing tabular generative models exhibit a clear tradeoff in the small-d…