3 papers
cs.CL2025
ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute
Hao Wen, Yifan Su, Feifei Zhang +4
Recent advances in Large Language Models (LLMs) have been driven by test-time compute scaling - a strategy that improves reasoning by generating longer, sequential thought processe…
cs.CV2025
DyCrowd: Towards Dynamic Crowd Reconstruction from a Large-scene Video
Hao Wen, Hongbo Kang, Jian Ma +5
3D reconstruction of dynamic crowds in large scenes has become increasingly important for applications such as city surveillance and crowd analysis. However, current works attempt…
cs.CL2024
ChainStream: An LLM-based Framework for Unified Synthetic Sensing
Jiacheng Liu, Yuanchun Li, Liangyan Li +5
Many applications demand context sensing to offer personalized and timely services. Yet, developing sensing programs can be challenging for developers and using them is privacy-con…