3 citations · 3 across the 4 of their papers we have counts for
4 papers
VLX-VR: An Agentic-Aware Video Reasoning Model
Sheng Li, Peng Liu, Qianqian Zhang +1
Real-world video understanding requires integrating visual, audio, textual, and temporal evidence distributed across a video. Yet many pipelines use a fixed video context and singl…
Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research
Qianqian Zhang, Jiajia Liao, Heting Ying +9
Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks. However, developing robu…
VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
Haozhan Shen, Peng Liu, Jingcheng Li +9
Recently DeepSeek R1 has shown that reinforcement learning (RL) can substantially improve the reasoning capabilities of Large Language Models (LLMs) through a simple yet effective…
OmChat: A Recipe to Train Multimodal Language Models with Strong Long Context and Video Understanding
Tiancheng Zhao, Qianqian Zhang, Kyusong Lee +7
We introduce OmChat, a model designed to excel in handling long contexts and video understanding tasks. OmChat's new architecture standardizes how different visual inputs are proce…