1 citations · 1 across the 11 of their papers we have counts for
13 papers
Understanding Interfirm AI Talent Flow Networks through Online Professional Profiles
Donghang Li, Yunhan Zheng, Alok Prakash +2
Artificial intelligence capabilities are often measured as resources accumulated within firms, yet they also circulate across organizational boundaries through worker mobility. Dra…
Plan-to-Synthesis: Cross-City Human Mobility Generation via Semantic Latent Flow Matching
Zhoufu Wang, Baoshen Guo, Zhiqing Hong +6
Human mobility generation aims to synthesize realistic point-of-interest (POI) visitation trajectories and has become an important tool for travel behavior modeling, transportation…
Enhancing the Socioeconomic Understanding of Foundation Models with Urban Mobility
Baoshen Guo, Donghang Li, Zhiqing Hong +4
Foundation models have recently been applied to urban socioeconomic prediction using POI text, satellite imagery, and geospatial descriptions. However, these models mostly rely on…
MeMo: Memory as a Model
Ryan Wei Heng Quek, Sanghyuk Lee, Alfred Wei Lun Leong +6
Large language models (LLMs) achieve strong performance across a wide range of tasks, but remain frozen after pretraining until subsequent updates. Many real-world applications req…
SENSE: Satellite-based ENergy Synthesis for Sustainable Environment
Kailai Sun, Mingyi He, Heye Huang +5
Urban Building Energy Modeling plays a critical role in achieving the United Nations' Sustainable Development Goals 7 and 11. Although existing studies based on satellite imagery a…
Envisioning global urban development with satellite imagery and generative AI
Kailai Sun, Yuebing Liang, Mingyi He +5
Urban development has been a defining force in human history, shaping cities for centuries. However, past studies mostly analyze such development as predictive tasks, failing to re…
Perception-Aware Multimodal Spatial Reasoning from Monocular Images
Yanchun Cheng, Rundong Wang, Xulei Yang +4
Spatial reasoning from monocular images is essential for autonomous driving, yet current Vision-Language Models (VLMs) still struggle with fine-grained geometric perception, partic…
MineDraft: A Framework for Batch Parallel Speculative Decoding
Zhenwei Tang, Arun Verma, Zijian Zhou +4
Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to propose draft tokens that are subsequently verified by a larger target model.…
Adaptive Problem Generation via Symbolic Representations
Teresa Yeo, Myeongho Jeon, Dulaj Weerakoon +4
We present a method for generating training data for reinforcement learning with verifiable rewards to improve small open-weights language models on mathematical tasks. Existing da…
ClassMind: Scaling Classroom Observation and Instructional Feedback with Multimodal AI
Ao Qu, Yuxi Wen, Jiayi Zhang +6
Classroom observation -- one of the most effective methods for teacher development -- remains limited due to high costs and a shortage of expert coaches. We present ClassMind, an A…
MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents
Zijian Zhou, Ao Qu, Zhaoxuan Wu +6
Modern language agents must operate over long-horizon, multi-turn interactions, where they retrieve external information, adapt to observations, and answer interdependent queries.…
TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding
Zhaoxuan Wu, Zijian Zhou, Arun Verma +3
We propose TETRIS, a novel method that optimizes the total throughput of batch speculative decoding in multi-request settings. Unlike existing methods that optimize for a single re…
DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning
Zijian Zhou, Xiaoqiang Lin, Xinyi Xu +3
In-context learning (ICL) allows transformer-based language models that are pre-trained on general text to quickly learn a specific task with a few "task demonstrations" without up…