Hao Wu

SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learning

Hao Wu, Kin Whye Chew, Yizhan Han, et al.

WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning

Haozhe Hu, Hao Wu, Peiran Yin, et al.

SciForge: An AI-Native, Multimodal Workbench for Scientific Discovery

SciForge Team, Zhangyang Gao, Minghao Fang, et al.

From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI

Yongheng Zhang, Ziang Liu, Jiaxuan Zhu, et al.

RoboAlign-R1: Distilled Multimodal Reward Alignment for Robot Video World Models

Hao Wu, Yuqi Li, Yuan Gao, et al.

AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories

Xue Xia, Chengkai Yao, Mingyu Tsoi, et al.

3DCity-LLM: Empowering Multi-modality Large Language Models for 3D City-scale Perception and Understanding

Yiping Chen, Jinpeng Li, Wenyu Ke, et al.

Em-Garde: A Propose-Match Framework for Proactive Streaming Video Understanding

Yikai Zheng, Xin Ding, Yifan Yang, et al.

Em-Garde is a new system for understanding video streams in real-time that can proactively answer user questions about what's happening in videos. Instead of checking every frame to decide when to respond (which is slow and inaccurate), it converts user questions into visual search patterns and efficiently matches them against the incoming video stream, achieving better accuracy with less computational effort.