From the 1 of 6 linked papers with an AI index.
6 papers
RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination
Haotian Liang, Mingkang Chen, Yufei Huang +27
The paper introduces RxBrain, a foundation model that jointly reasons over language and visual inputs to create embodied plans, using a multimodal Mixture-of-Transformers architect…
MM-ACT: Learn from Multimodal Parallel Generation to Act
Haotian Liang, Xinyi Chen, Bin Wang +12
A generalist robotic policy needs both semantic understanding for task planning and the ability to interact with the environment through predictive capabilities. To tackle this, we…
COSMO-RL: Towards Trustworthy LMRMs via Joint Safety and Stability
Yizhuo Ding, Mingkang Chen, Qiuhua Liu +7
Large Multimodal Reasoning Models (LMRMs) are moving into real applications, where they must be both useful and safe. Safety is especially challenging in multimodal settings: image…
VTPerception-R1: Enhancing Multimodal Reasoning via Explicit Visual and Textual Perceptual Grounding
Yizhuo Ding, Mingkang Chen, Zhibang Feng +4
Multimodal large language models (MLLMs) often struggle to ground reasoning in perceptual evidence. We present a systematic study of perception strategies-explicit, implicit, visua…
Truly Assessing Fluid Intelligence of Large Language Models through Dynamic Reasoning Evaluation
Yue Yang, MingKang Chen, Qihua Liu +9
Recent advances in large language models (LLMs) have demonstrated impressive reasoning capacities that mirror human-like thinking. However, whether LLMs possess genuine fluid intel…
SafeWork-R1: Coevolving Safety and Intelligence under the AI-45 Law
Shanghai AI Lab, :, Yicheng Bao +115
We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framewo…