9 papers
H-WM: Robotic Task and Motion Planning Guided by Hierarchical World Model
Jinbang Huang, Wenyuan Chen, Zhiyuan Li +9
World models are becoming central to robotic planning and control by predicting future state transitions. Existing approaches mainly rely on visual, latent, or language prediction,…
ContextRL: Enhancing MLLM's Knowledge Discovery Efficiency with Context-Augmented RL
Xingyu Lu, Jinpeng Wang, YiFan Zhang +12
We propose ContextRL, a novel framework that leverages context augmentation to overcome these bottlenecks. Specifically, to enhance Identifiability, we provide the reward model wit…
A Multimodal Large Language Model-Driven Framework for Context-Aware UAV Emergency Landing Site Selection
Chunliang Hua, Zeyuan Yang, Lei Zhang +4
Safe UAV emergency landing requires more than just identifying flat terrain; it demands understanding complex semantic risks (e.g., crowds, temporary structures) invisible to tradi…
Embodied Arena: A Comprehensive, Unified, and Evolving Evaluation Platform for Embodied AI
Fei Ni, Min Zhang, Pengyi Li +34
Embodied AI development significantly lags behind large foundation models due to three critical challenges: (1) lack of systematic understanding of core capabilities needed for Emb…
Kwai Keye-VL 1.5 Technical Report
Biao Yang, Bin Wen, Boyang Ding +58
In recent years, the development of Large Language Models (LLMs) has significantly advanced, extending their capabilities to multimodal tasks through Multimodal Large Language Mode…
Thyme: Think Beyond Images
Yi-Fan Zhang, Xingyu Lu, Shukang Yin +17
Following OpenAI's introduction of the ``thinking with images'' concept, recent efforts have explored stimulating the use of visual information in the reasoning process to enhance…
Kwai Keye-VL Technical Report
Kwai Keye Team, Biao Yang, Bin Wen +57
While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities on static images, they often fall short in comprehending dynamic, information-dense short-form vi…
Why Distillation can Outperform Zero-RL: The Role of Flexible Reasoning
Xiao Hu, Xingyu Lu, Liyuan Mao +6
Reinforcement learning (RL) has played an important role in improving the reasoning ability of large language models (LLMs). Some studies apply RL directly to \textit{smaller} base…
R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement Learning
Yi-Fan Zhang, Xingyu Lu, Xiao Hu +13
Multimodal Reward Models (MRMs) play a crucial role in enhancing the performance of Multimodal Large Language Models (MLLMs). While recent advancements have primarily focused on im…