7 papers · 1 filter
CAR-VLA: Complexity-Aware and Risk-Adaptive Reasoning for Autonomous Driving
Xiaolei Chen, Zhuolin He, Yuxuan Liang +14
Existing adaptive reasoning methods for driving Vision-Language-Action (VLA) models primarily focus on whether to reason, overlooking how reasoning should differ across driving sit…
Beyond the Eye: Efficient Multimodal Reasoning via Self-Regulated Implicit Visual Tools
Xiuwei Chen, Quanlin Chen, Wentao Hu +8
Recent multimodal large language models (MLLMs) have made remarkable progress on fine-grained perception tasks under the "Thinking with Images" (TwI) paradigm by iteratively perfor…
Latent Visual States for Efficient Multimodal Reasoning
Xiuwei Chen, Wentao Hu, Yongxin Wang +8
The integration of visual evidence has significantly enhanced the capabilities of large multimodal models. However, this integration predominantly relies on generating discrete out…
Intend, Reflect, Refine: An Adaptive Multimodal Reflection Framework for Autonomous Driving
Zisheng Chen, Yuping Qiu, Jianhua Han +6
Recent Vision-Language-Action (VLA) models have advanced end-to-end autonomous driving by incorporating reasoning for better interpretability and planning quality. However, most ex…
SyncLoop: A Multimodal Dual-Loop Framework for Self-Improving Mathematical Reasoning
Xiuwei Chen, Wentao Hu, Hanhui Li +9
Recent advances in multimodal large language models (MLLMs) have shown impressive reasoning capabilities. However, further enhancing existing MLLMs necessitates high-quality vision…
SemHiTok: A Unified Image Tokenizer via Semantic-Guided Hierarchical Codebook for Multimodal Understanding and Generation
Zisheng Chen, Chunwei Wang, Runhui Huang +6
In this paper, we introduce SemHiTok, a unified image Tokenizer via Semantic-Guided Hierarchical codebook that provides consistent discrete representations for multimodal understan…
TransMamba: Fast Universal Architecture Adaption from Transformers to Mamba
Xiuwei Chen, Wentao Hu, Xiao Dong +7
Transformer-based architectures have become the backbone of both uni-modal and multi-modal foundation models, largely due to their scalability via attention mechanisms, resulting i…