6 papers
Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack
He Zhang, Lingzhu Xiang, Haitao Lin +23
In this report, we present Hy-Embodied-0.5-VLA, abbreviated as HyVLA-0.5, an end-to-end system that spans the full robot learning stack: data collection, model design, continued pr…
Insight-V++: Towards Advanced Long-Chain Visual Reasoning with Multimodal Large Language Models
Yuhao Dong, Zuyan Liu, Shulin Tian +2
Large Language Models (LLMs) have achieved remarkable reliability and advanced capabilities through extended test-time reasoning. However, extending these capabilities to Multi-mod…
Ola: Pushing the Frontiers of Omni-Modal Language Model
Zuyan Liu, Yuhao Dong, Jiahui Wang +4
Recent advances in large language models, particularly following GPT-4o, have sparked increasing interest in developing omni-modal models capable of understanding more modalities.…
Unveiling the Compositional Ability Gap in Vision-Language Reasoning Model
Tianle Li, Jihai Zhang, Yongming Rao +1
While large language models (LLMs) demonstrate strong reasoning capabilities utilizing reinforcement learning (RL) with verifiable reward, whether large vision-language models (VLM…
Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models
Yuhao Dong, Zuyan Liu, Hai-Long Sun +4
Large Language Models (LLMs) demonstrate enhanced capabilities and reliability by reasoning more, evolving from Chain-of-Thought prompting to product-level solutions like OpenAI o1…
BREEN: Bridge Data-Efficient Encoder-Free Multimodal Learning with Learnable Queries
Tianle Li, Yongming Rao, Winston Hu +1
Encoder-free multimodal large language models(MLLMs) eliminate the need for a well-trained vision encoder by directly processing image tokens before the language model. While this…