5 papers
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…
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…
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…
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.…
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…