2 citations · 2 across the 12 of their papers we have counts for
12 papers
ME-Brain-1.0: Memory, Cognition and Action for Evolving Embodied Intelligence
Wei He, Hengtao Li, Zhongrui Yu +20
Current embodied systems largely rely on pretrained capabilities that remain fixed after deployment, limiting their ability to learn from physical interaction. We introduce MachEmb…
Skill-Use: Can LLMs Actually Use Skills in Agentic Harnesses?
Jinyi Han, Yuanjian Xu, Ying Liao +6
Large language model (LLM) agents increasingly rely on skills, structured documents that specify when to act, which procedure to follow, and which tools are allowed. Existing evalu…
From Foundation to Application: Improving VLA Models in Practice
Wei Wu, Fangjing Wang, Fan Lu +21
Despite recent progress of VLA foundation models, the disparity between laboratory conditions and real-world applications continues to impede their practical implementation. To bri…
VisualNeedle: Benchmarking Active Visual Search in Information-Dense Scenes
Jingru Chen, Yiming Liu, Mingtao Chen +5
Frontier multimodal large language models (MLLMs) have been reported to achieve over 90\% accuracy on fine-grained perception benchmarks. However, such scores do not necessarily im…
Self-Consistent Latent Reasoning: Long Latent Sequence Reasoning for Vision-Language Model
Chenfeng Wang, Wei He, Xuhan Zhu +10
In language reasoning, longer chains of thought consistently yield better performance, which naturally suggests that visual latent reasoning may likewise benefit from longer latent…
Visually-grounded Humanoid Agents
Hang Ye, Xiaoxuan Ma, Fan Lu +3
Digital human generation has been studied for decades and supports a wide range of real-world applications. However, most existing systems are passively animated, relying on privil…
A Pragmatic VLA Foundation Model
Wei Wu, Fan Lu, Yunnan Wang +22
Offering great potential in robotic manipulation, a capable Vision-Language-Action (VLA) foundation model is expected to faithfully generalize across tasks and platforms while ensu…
Vision-Centric Activation and Coordination for Multimodal Large Language Models
Yunnan Wang, Fan Lu, Kecheng Zheng +4
Multimodal large language models (MLLMs) integrate image features from visual encoders with LLMs, demonstrating advanced comprehension capabilities. However, mainstream MLLMs are s…
Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning
Fan Lu, Wei Wu, Kecheng Zheng +7
Generating detailed captions comprehending text-rich visual content in images has received growing attention for Large Vision-Language Models (LVLMs). However, few studies have dev…
Learning Visual Generative Priors without Text
Shuailei Ma, Kecheng Zheng, Ying Wei +7
Although text-to-image (T2I) models have recently thrived as visual generative priors, their reliance on high-quality text-image pairs makes scaling up expensive. We argue that gra…
LoTLIP: Improving Language-Image Pre-training for Long Text Understanding
Wei Wu, Kecheng Zheng, Shuailei Ma +7
Understanding long text is of great demands in practice but beyond the reach of most language-image pre-training (LIP) models. In this work, we empirically confirm that the key rea…
DreamLIP: Language-Image Pre-training with Long Captions
Kecheng Zheng, Yifei Zhang, Wei Wu +5
Language-image pre-training largely relies on how precisely and thoroughly a text describes its paired image. In practice, however, the contents of an image can be so rich that wel…